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Academic research is crucial to the development of science and technology and is an important factor that affects national strength. When writing an academic research paper, a rhetorical structure is typically used to present the paper's ideas, but this task is quite difficult for junior researchers. To solve this problem, some studies have adopted text mining to assist with the writing, but the existing methods still require human intervention to generate sentences. Recently, due to the increasing maturity of deep learning technology and the ability to address the problem of automatic text generation, progress has been made in this area. The highly complex deep learning operations can correctly generate sequences and find correlations between sequences. When a user provides a few keywords and key sentences, the proposed algorithm can generate an introduction section for the user. The results show that the generated introduction is more coherent, clearer, and more fluent than existing summarization methods. In addition, the method proposed in this study improves the accuracy compared with traditional text extraction methods. The manuscript produced by this study has been evaluated to show that the study can produce a comprehensive introduction compared with previous studies. • The proposed framework can generate a comprehensive introduction based on users' brief inputs. • This paper demonstrates a three-testing-threshold approach to check the content quality. • The introduction generated through this method has less than 3% similarity to the original text by comparison. [ABSTRACT FROM AUTHOR]

Reviewers play a significant role in academic peer review activities, including conference paper assignment and funding selection, because their evaluation of proposals impacts the final decision. Several studies have proposed reviewer selection strategies or reviewer evaluation methods for solving the problem of selecting appropriate reviewers. Identifying reviewers who are familiar with the proposals to be reviewed is the objective of the reviewer assignment problem. However, the majority of the existing studies ignore quantitative constraints with respect to the articles assigned to the reviewers during the review process. In this study, we propose a novel optimization model with several review condition constraints to address the reviewer assignment problem. In the proposed model, the expertise and research areas of the candidate reviewers and proposals are identified using semantic topic models, which are demonstrated to be effective when measuring the relevance of the reviewers with respect to the proposals to be reviewed; further, the computational efficiency is improved owing to the reduced representation dimensionality. Herein, an improved heuristic algorithm is proposed to match reviewers and papers based on specific topic areas, and candidate reviewers are assigned to each proposal under the global optimum condition based on their overall performance values. Subsequently, an empirical test is conducted using a conference reviewer dataset. The obtained results show that the proposed model can help the managers to efficiently and effectively select reviewers in terms of the convergence rate and convergence level when compared with several classic benchmarks. • A novel optimization model is proposed to address the reviewer assignment problem. • The expertise and profiles are identified with the semantic topic models. • An improved heuristic algorithm is proposed to assign reviewer with global optimum. • The effectiveness and robustness is validated in three stages of experiments. [ABSTRACT FROM AUTHOR]

This paper is concerned with a variant of the traveling salesman problem (TSP) called the maximum scatter traveling salesman problem (MSTSP). The goal of MSTSP is to find a Hamiltonian cycle that maximizes the minimum length edge of the cycle. This problem has real-world applications in domains such as manufacturing and medical imaging. Both symmetric and asymmetric versions of the problem are considered in this paper. To address this problem, we have developed two evolutionary approaches. Our first approach is based on a genetic algorithm, whereas the second approach is based on a differential evolution algorithm. Initial solution generation procedure, variation operators (crossover and mutation) used in our approaches are designed considering the characteristics of this problem. The solutions obtained through variation operators are improved further through a local search that is specially adapted for MSTSP. For benchmarking, we have compared the performance of our proposed approaches with the state-of-the-art approaches available in the literature. Our approaches obtained better quality solutions in a shorter time for both symmetric and asymmetric versions of the problem, thereby clearly demonstrating their effectiveness in solving MSTSP. • This paper is concerned with the maximum scatter traveling salesman problem (MSTSP). • Two evolutionary approaches are proposed for solving this problem. • First approach is a steady-state genetic algorithm, while the latter is a discrete differential evolution algorithm. • Various components of our approaches are designed considering the characteristics of MSTSP. • Computational results show the effectiveness of our proposed approaches. [ABSTRACT FROM AUTHOR]

This paper presents a novel optimal reference tracking control approach resulted from the combination of a popular policy gradient Reinforcement Learning (RL) algorithm, namely Proximal Policy Optimization (PPO), and a metaheuristic Slime Mould Algorithm (SMA). One of the most important parameters in the PPO-based RL process is the learning rate, which has a big impact on how the parameters of the actor neural network (NN) are iteratively updated. In every episode of the RL process, the weights and the biases of the actor NN are multiplied with the learning rate, determining how much the learning agent will step into a certain direction computed based on previous experiences. The classical PPO algorithm usually relies on fixed values for the learning rates which rarely change, or not at all, during the learning process. However, its main drawback is that the learning agent cannot take advantage of positive momentum in the learning process by accelerating towards good learning experiences or slow down and quickly change the direction in the case of consecutive negative learning experiences. The main objective of the combination proposed in this paper is to create an adaptive SMA-based PPO approach applied to control systems, which instead of using fixed learning rate values, it uses the SMA to compute optimal values of the learning rates in each time step of the learning process based on the progress of the learning agent. This paper investigates if the adaptive SMA-based PPO control approach can be considered as an alternative to the classical PPO version, which employs fixed values of the learning rate. A comparison is carried out using control system performance indices gathered while performing an optimal reference tracking control task on tower crane system laboratory equipment. [Display omitted] • A combination of Proximal Policy Optimization and metaheuristic SMA is given. • The adaptive approach mitigates the drawbacks of constant learning rates. • SMA computes optimal values of learning rates in each learning process step. • A comparison with classical Proximal Policy Optimization is carried out. • The validation is done on real-time nonlinear tower crane position control. [ABSTRACT FROM AUTHOR]

With the advancement of remote sensing technology, the amount of remote sensing image (RSI) has increased sharply. The explosion in data volume requires higher standards of image compression and encryption technology. This paper proposes a lossless and lossy encryption-compression method for RSI. Because RSIs contain important and unimportant information, using a normal algorithm could ignore their important information or reduce the computational efficiency, so we use the trained model by DeepLabv3+ to segment the region of interest (ROI) and the region of non-interest (RONI), and implement lossless and lossy algorithms respectively. The whole algorithm is based on the newly proposed hyperchaotic system 2D Tent coupled Infinite collapse map (2D TICM). According to the 2D TICM, the dynamic 3D Latin cube is generated. This cube is then used for dynamic scrambling algorithms between multiple planes (3D LMBS), within planes (3D LMIS), and for the multiple dynamic S boxes substitution algorithm. Due to the large size of RSI, the image is divided into blocks, and MATLAB parallel mechanism is used to encrypt each block at the same time. First, the lossy part is scrambled by 3D LMIS, then compressed with 2D compressive sensing (2D CS). Finally, each block is substituted with a different S box. A CS reconstruction algorithm combining decryption and reconstruction, 2D projection gradient chaotic decryption algorithm (2D PG-CD), is proposed. The lossless part embeds the encryption into JPEG-LS, encrypts while compressing, and then encrypts according to 3D LMBS and S boxes. The algorithm in this paper considers the characteristics of RSI, experiments show that it has good security and compression performance. • A hyperchaotic system 2D Tent coupled infinite collapse map (2D-TICM) is proposed. • Lossy compression 2D CS and 2D PG-CD improves the computational efficiency. • Encryption step is integrated into JPEG-LS and compression to retain important information. • Dynamic 3D Latin cube is designed with chaos and multiple S boxes substitution are proposed. [ABSTRACT FROM AUTHOR]

Normalization of objectives plays a crucial role in evolutionary multi-objective optimization (EMO) to handle objective functions with different scales, which can be found in real-world problems. Although the effect of normalization methods on the performance of EMO algorithms has been investigated in the literature, that of preference-based EMO (PBEMO) algorithms is poorly understood. Since PBEMO aims to approximate a region of interest, its population generally does not cover the Pareto front in the objective space. This property may make normalization of objectives in PBEMO difficult. This paper investigates the effectiveness of three normalization methods in three representative PBEMO algorithms. We present a bounded archive-based method for approximating the nadir point. First, we demonstrate that the normalization methods in PBEMO perform significantly worse than that in conventional EMO in terms of approximating the ideal point, nadir point, and range of the PF. Then, we show that PBEMO requires normalization of objectives on problems with differently scaled objectives. Our results show that there is no clear "best normalization method" in PBEMO, but an external archive-based method performs relatively well. • This paper investigates the effectiveness of three normalization methods in three representative PBEMO algorithms. • We demonstrate that the normalization methods in PBEMO perform significantly worse than that in conventional EMO in terms of approximating the ideal point, nadir point, and range of the PF. scaled objectives. • We show that there is no clear "best normalization method" in PBEMO, but an external archive-based method performs relatively well. [ABSTRACT FROM AUTHOR]

The 0–1, Multidimensional Knapsack Problem (MKP) is a classical NP-hard combinatorial optimization problem with many engineering applications. In this paper, we propose a novel algorithm combining evolutionary computation with the exact algorithm to solve the 0–1 MKP. It maintains a set of solutions and utilizes the information from the population to extract good partial assignments. To find high-quality solutions, an exact algorithm is applied to explore the promising search space specified by the good partial assignments. The new solutions are used to update the population. Thus, the good partial assignments evolve towards a better direction with the improvement of the population. Extensive experimentation with commonly used benchmark sets shows that our algorithm outperforms the state-of-the-art heuristic algorithms, TPTEA and DQPSO , as well as the commercial solver CPlex. It finds better solutions than the existing algorithms and provides new lower bounds for 10 large and hard instances. • We propose an efficient and practical algorithm for solving the 0–1 Multidimensional Knapsack Problem in this paper. • The algorithm takes advantages of Evolutionary Computation and Large Neighborhood Search. • It works well in solving the large and hard 0–1 MKP instances. • We provide new lower bounds (best known results) for 10 large and hard instances from the commonly used benchmark set. [ABSTRACT FROM AUTHOR]

Elevator systems in buildings face challenges due to unpredictable passenger flow, which can make scheduling elevators complicated to optimize their operation. Most of the existing algorithms are developed based on pattern recognition and may not be effective in scenarios where patterns are difficult to classify, especially when elevator operations involve uncertain human behaviors. To address this issue, this paper proposes a time-dependent optimization model that considers long-term and multi-floor peaks, with a focus on peak floors. The proposed model accounts for dynamic scheduling patterns of elevators and represents passenger flow direction as a relation matrix. The proposed direction optimization method contains several functions to guide iteration direction and improve the efficiency of an iteration process based on classical algorithms. This method also ensures the stability of Markov chains by adjusting an iteration process. The feasibility of the proposed method is supported by the relevant theory, and experimental results show that the direction-optimized algorithms outperform classical algorithms, resulting in the superb operating efficiency of elevators at a lower cost. This paper contributes to the development of efficient algorithms for scheduling elevators in complex traffic patterns, which can improve performance of elevator group systems in buildings. The proposed method is not only limited to elevators but can also be extended to other transportation systems with flow requirements. • Estimating the relation between peak floors without pattern recognition. • Four relations are defined to classify the passenger flow. • Dynamically modifying three metaheuristic algorithms' iteration law. • The application for transportation systems with unpredictable flow. [ABSTRACT FROM AUTHOR]

Rough set is an important method for dealing with incomplete information systems. In incomplete information systems, the most common way to determine the relation between two samples is the tolerance relation. However, the condition for the tolerance relation to determine those samples may belong to the same category is very lenient, which makes the reduction rate low when using the rough set generated by this relation to select features. In response to the above problems, we design the neighborhood equivalence tolerance relation to solve them. Different from other improved tolerance relations, firstly, the relation designed in this paper does not require additional threshold to accomplish the above goals, which will avoid the trouble caused by the given threshold. Secondly, we notice that most of the current improvements for this kind of problems are computationally cumbersome, and the relation designed in this paper is simple and effective. Based on this, we construct a neighborhood rough set model that handles incomplete information by using this relation, introduce its properties, expound the properties that a reduction set should satisfy, quantify the importance of conditional attributes with attribute dependence degree, which provides the basis for the design of feature selection algorithm. Finally, the greedy strategy is used to design a forward feature selection algorithm. Experimental results show that the model is effective in dealing with incomplete information systems. The feature selection algorithm has the smallest size of the average reduced subset on twelve datasets, and maintains the accuracy of the classifier, which verifies that the feature selection algorithm can effectively deal with incomplete information systems. ● As the judgment of tolerance relation is very lenient, the existing improvement methods either rely on the probability distribution of attribute values in the information system, or need to give a additional threshold. To solve the above problems, this paper improves the tolerance relation, and the improved binary relation only relies on the existing information system itself, without giving additional threshold. ● Rough set in incomplete information systems can only select more attributes to refine the knowledge granules when making feature selection due to the lenient judgment of tolerance relation, which is equivalent to making up for the problems caused by tolerance relation when selecting attributes, but this will lead to low reduction rate. In this paper, we use the newly proposed binary relation to construct neighborhood rough set model in incomplete information systems, and use this model to make feature selection, which improves the problem of low reduction rate above, while maintaining the accuracy of the classifier. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive and new region proposal algorithm for generating high-quality regions. The main aim of this algorithm is to investigate different features in the proposal generation process. This algorithm is based on bottom-up image segmentation and deep learning techniques. Each primary region is represented by features derived from a convolutional neural network (CNN). The adjacent and similar regions are merged based on a new proposed searching algorithm and a distance function in a hierarchical way. Additionally, various hand-crafted texture features are examined for representing each region. These texture features are not being utilized previously in region proposal generation. This method also applied both texture-based and deep learning-based features, contemporaneously. Furthermore, the proposed region proposal algorithm was evaluated on two significant challenging datasets, including VOC2012 and COCO2017. The resulting proposals show more high-quality regions in high overlaps in comparison to previous region proposal algorithms. More importantly, the results verified that the deep learning-based features together with handcrafted texture features are complement and this fusion can overcome the shortcoming of other approaches. Additionally, the proposed region proposal algorithm is employed in weakly supervised semantic segmentation. The new generated proposals are used to create some new labeled masks. These masks are very useful and effective in the training phase of deep learning. This approach is evaluated on VOC2012 and compared with previous region-based and free region-based methods. The results show the efficacy of the proposed method. • This paper proposes a new region proposal generation based on a hierarchical deep learning-based merging algorithm. • The effectiveness and quality of some known texture-based descriptors are explored in the proposed algorithm. • A new algorithm is introduced to generate pseudo training data using weak supervision and region proposal. • The proposed region proposal algorithm was evaluated on two significant challenging datasets, including VOC2012 and COCO2017. [ABSTRACT FROM AUTHOR]

Negative selection algorithms play an important role in anomaly detection. Interface detectors are a special negative selection algorithm that completely eliminates outer holes, but there are detection blind areas. This paper proposes a negative selection algorithm with hypercube interface detectors for anomaly detection. It uses self-sample clusters to construct self-space, and boundary self-sample clusters to describe the interface detectors. It eliminates the detection blind area and improves the detection rate. To validate the performance of the proposed method, experiments were conducted using the iris dataset, the skin segmentation dataset, the Breast Cancer of Wisconsin (BCW) dataset, and the Waveform Database Generator (Version 2) dataset. Experimental results show that the proposed method in this paper has a higher detection rate, lower false alarm rate, and fewer detectors than other anomaly detection methods for the same parameters. • HI-detector breaks away from the hyperspherical detector used by traditional negative selection algorithms. • HI-detector consists of a cluster of multiple hypercube self-samples. • HI-detector completely eliminates duplicate coverage of holes and detectors. • HI-detector has only one parameter that needs to be set. [ABSTRACT FROM AUTHOR]

The objective of image hiding is to conceal a secret image in a cover image inconspicuously. The challenges for image hiding focus on the embedding capacity, image quality and security. To contend with these challenges, this paper presents a novel image hiding method using Invertible Rescaling Net (IRN) and Similarity of Bits Pairs (SBP). Because of the downscaling ability of IRN, our method is able to hide a secret image into a cover image with the same dimensions. Moreover, the superior rescaling capabilities of IRN enable us to produce a recovered image of high-quality. We improve the SBP embedding technique and combine it with differential coding to boost the quality of the stego image. In the experiments, the quality of the recovered secret image and stego image is assessed using various quality evaluation metrics, and for the security, the anti-steganalysis performance is also evaluated in this paper. Extensive experimental results reveal that the quality of both the recovered secret image and the stego-image in our method outperforms several advanced methods and provides a high degree of security. • We apply invertible rescaling net (IRN) to image hiding task for the first time. • We improve the SBP encoding algorithm to prevent the degradation of the quality of the stego image. • Our method considers the robustness against the classical adversary's steganalysis. [ABSTRACT FROM AUTHOR]

The widespread development of monitoring devices in power system has indeed led to the generation of large amounts of power consumption data. Storing and transmitting this enormous volume of data has become a significant challenge for power system operators and researchers. Although high data rates are available for transmission, data compression is still necessary for power system applications to lessen the load of data transmission and storage. The wavelet transform plays an important role in data compression for power system applications. This paper presents an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. In general, some wavelet coefficients are neglected to produce DWT-based data compression by using hard or soft thresholds. But the volume of compressed data differs for various threshold values. In this context, selecting an optimal threshold is a challenging task in power system data compression. This paper proposes a multi-objective particle swarm optimisation (MO-PSO) for optimal threshold selection. The proposed approach is tested using IEEE power quality wave data sets and Dutch Residential Energy Datasets (DRED). The proposed MO-PSO algorithm finds the global optimum threshold, and it outperforms the other existing algorithms. The proposed algorithm efficiently compresses the test dataset with a maximum compression ratio (CR) of 2.07. • The development of an adaptive algorithm for data compression is applied in power system data. • MO-PSO algorithm is proposed to find the optimal threshold in WT based data compression. • Results are compared with recent research works. • The efficiency of data compression algorithm is evaluated by using compression ratio, mean square error and signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Economic dispatch (ED) has received considerable interest in the field of energy management and optimization. The problem aims to determine the most cost-effective power allocation strategy that satisfies the power demand and all physical constraints of the power system. To solve this problem, we propose an algorithm based on differential evolution and adopt a hybrid mutation strategy, a linear population size reduction mechanism, and an improved single-unit repair mechanism. Experimental results confirmed that these mechanisms are useful for performance improvement. The proposed algorithm (L -HMDE) showed good performance when compared with more than 90 algorithms in solving 22 test cases. It could provide high-quality solutions stably and efficiently. In addition to designing a good algorithm, we present a review of over 100 papers and highlight their algorithm features. We also provide a comprehensive collection of test cases in the literature. Through careful examination and verification, data coefficients of these test cases and solutions to them are included in this paper as a useful reference for researchers who are interested in this problem. • A differential evolution-based algorithm (L-HMDE) is proposed to address the economic dispatch problem. • The L-HMDE integrates a hybrid mutation strategy, a population size reduction mechanism, and an improved repair procedure. • It shows good solution quality and high efficiency when compared with more than 90 existing algorithms on 22 test cases. • A comprehensive collection of test cases and solutions is also provided. [ABSTRACT FROM AUTHOR]

Aiming at the problems of single optimization strategy and poor comprehensive performance of MOFA, multi-objective firefly algorithm with adaptive region division is proposed in this paper. By leveraging the convergence index, our algorithm intelligently divides the dominant and non-dominant solution groups into three sub-regions, namely balance, exploration, and development areas, each with a distinct learning strategy that complements the strengths of fireflies. Specifically, fireflies in the balance area learn from global optimal particles with diversity to achieve a balanced exploration and development ability. Fireflies in the exploration area jointly learn from globally optimal particles with convergence and diversity, increasing the algorithm's likelihood of discovering Pareto optimal solutions. Lastly, fireflies in the development area rapidly converge under the guidance of the globally optimal particle of convergence, thus improving the algorithm's development ability. To further enhance the comprehensive optimization performance, we introduce a novel fusion index as an external archive update strategy that preserves solutions with superior convergence and diversity. Our experiments on 20 benchmark functions and a multi-objective optimization power flow example demonstrate that our algorithm outperforms other multi-objective optimization algorithms, highlighting its superior optimization performance. • This paper proposes a multi-objective firefly algorithm with adaptive region division. • Based on the idea of adaptive region division, the firefly population was divided into adaptive regions and regions, and multiple learning strategies were integrated. • Our algorithm has higher performance than the classical MOEA on instances of benchmarking functions and multi-objective optimization currents. [ABSTRACT FROM AUTHOR]

Recently, practitioners and researchers have concentrated on the use of meta-heuristics in the design of multi-objective supply chain networks. The number of publications in this field reflects this interest. The purpose of this paper is to propose a novel metaheuristic approach for designing supply chain network problems in the case of multi-objective supply chains. The proposed algorithm hybridizes three meta-heuristic approaches; simulated annealing, tabu search, and variable neighborhood algorithms. The proposed algorithm is also combined with a linear programming approach. The purpose of hybridization is to aggregate the approaches in order to benefit from the advantages of these solution algorithms. The features of these algorithms are investigated in this paper for the first time. The most useful characteristics of each algorithm are then utilized for developing the new algorithm. The performance of the proposed algorithm is compared with an exact algorithm, simulated annealing, and tabu search algorithms. The results indicated that the proposed algorithm is comparable to the exact in the case of small and medium-sized supply chain problems. The findings revealed that the proposed algorithm takes on an average of 6.3 min to solve small and medium problems, while the exact algorithm takes 412 min. Moreover, the proposed algorithm outperforms other algorithms with a mean ideal distance (MID) of 13,606,871, a percent of domination (POD) of 0.84, and a computational time of 15.64 min compared to the tabu search algorithm's MID of 15,574,523, a POD of 0.55, and a computational time of 21.46 min. The simulated annealing algorithm, on the other hand, achieved an average MID of 18,145,931, a POD of 0.40, and a computational time of 30 min. Furthermore, the findings showed that the proposed algorithm is capable of solving complex and large supply chain problems in a reasonable time when exact approaches fail. [Display omitted] • Developing a novel metaheuristic algorithm for solving the MOSC network. • Optimizing the proposed algorithm parameters using regression models. • Comparing the proposed algorithm with other algorithms. • Confirming the performance of the proposed algorithm for solving MOSC problems. [ABSTRACT FROM AUTHOR]

This paper presents a new training algorithm that can update the situation of layers' network, and therefore, connections, neurons, and firing rate of neurons based on FORCE (first-order reduced and controlled error) training algorithm. The Quantized Full FORCE algorithm (QFF) also updates the number of neurons and connections between different layers in the network per iteration in a way that the whole firing rate of each layer is updated via selecting the best neurons and combining strong features. The update method is sequential, so that with each instance passing through the network, the network structure is updated with the Full FORCE algorithm. The algorithm updates the structure of networks with a multiple/single middle layer of the supervised version of feed forward networks such as Multilayer perceptron (MLP), changing them into partially-connected networks. A combination of principal component analysis PCA and Linear Discriminant Analysis (LDA) algorithms has been used to cluster the network input features. The paper focuses on the deep supervised MLP network with backpropagation (BP) and various datasets and its comparison with other MLP based stat of art methods and hybrid evolutionary algorithms. We achieved 98.15 percent accuracy for facial expression 98.6 and 97.7 percent for Wisconsin breast Cancer and Iris Flower in respectively. The training algorithm employed in the study enjoys a lower computational complexity while yielding faster and more accurate convergence, starting with a very low level of errors of 0.009 in comparison with the full connection network and it solves the challenge of getting stuck in local minima and poor convergence of Gradient Decent with BP. • Providing the new training algorithm to update the connections and firing rate of neurons in each iteration. • The proposed training model is based on Quantized Full FORCE (first-order reduced and controlled error) training. • Providing an accurate framework to update the number of neurons, converting the network to a partial connection network. • In the new training model, the best features have the highest number of connections and neurons within the middle layers. • Being the decision speed of the network in the proposed method much faster than a fully connected network. • Cost-effectiveness and much lower resource requirements for implementation of deep networks on hardware such as FPGAs. [ABSTRACT FROM AUTHOR]

In the cross-mutation phase of the Artificial Bee Colony optimization algorithm (ABC algorithm), the value of parameter φ is very important to the stability of ABC algorithm, however, most literatures did not specify the origin of the value range of parameter φ. Through the theoretical analysis of the stability of the ABC algorithm, appropriate parameters can be selected for the algorithm under different convergence conditions and different application conditions, and then the rapid convergence of the algorithm can be achieved, so the stability analysis of the algorithm is very worth exploring. This paper firstly introduces the ABC algorithm, and then differentiates the motion trajectory equations of employed bees in the ABC algorithm to obtain a differential model; then uses the discrete system stability theory to analyze the stability of the system, and obtains the relationship between the value range of the parameters in the cross-mutation stage and the stability of the algorithm. Finally, the correctness of the conclusion will be verified by experiments using the test function. The results obtained show that ABC algorithm with φ ∈ (− 2 , 0) converges faster, and has better global optimization performance than ABC algorithm with other values of φ , which are investigated in this paper. • Stability range of parameters can be obtained and realize the fast convergence through stability analysis of ABC algorithm. • The differential model system of employed bee movement track in ABC algorithm using the difference method innovatively. • Stability of the system is analyzed using the theory of stability for discrete dynamics system, also ranges of φ i j is obtained. [ABSTRACT FROM AUTHOR]

Determining the path for an evolutionary algorithm is crucial for its performance. Current methods for sampling reference points guiding evolutionary algorithms are inadequate for dealing with convex and concave Pareto fronts,and the uniformity of sampling results decreases significantly in high-dimensional spaces. In this paper,we propose a reference point sampling method based on angular relationships to tackle these issue. And we propose the concept of optimally distributed individuals based on the IGD indicator to ensure the distribution of the evolutionary process and prevent the algorithm from converging to local optima. Additionally,we introduce a novel method for calculating individuals' fitness within the population,ensuring convergence,uniformity,and distribution of the evolutionary algorithm,thereby enhancing selection pressure among non-dominated individuals. Experimental results on diverse benchmark test problems demonstrate that the proposed algorithm competes favorably with six advanced evolutionary algorithms for many-objective optimization. • A new reference point sampling method based on angular relationship is proposed. • The concept of optimal distributive solution is proposed to expand search space. • A new method for calculating individual fitness is proposed to select offspring. • The algorithm is extremely competitive with five advanced algorithms for MaOPs. [ABSTRACT FROM AUTHOR]

Surrogate-assisted multi-objective optimization algorithms have attracted widespread attention due to their outstanding performance in computationally expensive real-world problems. However, there is relatively little research about multi-objective optimization with complex and expensive constraints. Hence, a data-driven co-evolutionary exploration (DDCEE) algorithm is presented in this paper for the above-mentioned problems, where Radial Basis Functions are utilized to train dynamically updated surrogate models for each objective and constraint. Specifically, a data-driven co-evolutionary exploration framework is proposed to fully utilize and mine the potential available information of RBF models, and RBF models are constantly updated to guide co-evolutionary in discovering valuable feasible regions and achieving global optimization. In co-evolutionary exploration, one population focuses on exploring the entire space without considering constraints, while the other population focuses on exploring feasible regions and collaborating by sharing their respective offspring. Reference vectors are introduced in co-evolutionary exploration to divide the objective space into several sub-regions for further selection. Furthermore, an adaptive selection of promising samples strategy is presented to reasonably utilize the information of solutions with good convergence and enhance the convergence and diversity of the Pareto front. After comprehensive experiments on constrained multi/many-objective benchmark cases and an engineering application problem, DDCEE shows more stable and impressive performance when compared with five state-of-art algorithms. ● A notably effective data-driven co-evolutionary exploration algorithm is proposed to solve expensive CMOPs. ● The data-driven co-evolutionary exploration framework effectively handles complex constrained multi-objective problems. ● An adaptive selection strategy for promising samples is presented to enhance the convergence and diversity of the PF. [ABSTRACT FROM AUTHOR]

The key to solving constrained multiobjective optimization problems (CMOPs) lies in maintaining the feasibility, convergence, and diversity of the population. In recent years, various constraint handling techniques (CHTs) and strategies have been proposed to enhance the performance of constrained multiobjective evolutionary algorithms (CMOEAs). However, most of these algorithms face difficulties in dealing with problems that have large infeasible regions and discontinuous small feasible regions, as they have trouble crossing large infeasible regions while simultaneously maintaining the convergence and diversity of the population. To tackle this issue, this paper proposes a dual-population auxiliary coevolutionary algorithm with an enhanced operator, denoted as DAEAEO. Auxiliary population 1 employs an improved ϵ -constraint handling technique to provide high-quality feasible solutions for the main population. Auxiliary population 2 adopts the non-dominated sorting method to provide favorable objective information for the main population to help it cross the infeasible region. In addition, to further improve diversity, each population adopts an enhanced operator and a genetic operator to generate offspring, respectively. Finally, knowledge transfer between offspring is realized. Compared to six state-of-the-art CMOEAs on DASCMOPs, LIR-CMOPs, DOC test suites, and two real-world problems, the proposed DAEAEO achieved superior performance, especially for CMOPs with large infeasible regions and discontinuous small feasible regions. • A constrained multiobjective coevolutionary algorithm is proposed, termed DAEAEO. • A dual-population auxiliary evolutionary strategy is proposed. • The use of knowledge transfer promotes information interaction among offspring. • An enhanced search operator is developed to balance convergence and diversity. • The effectiveness of DAEAEO is verified on test suites and two real-world problems. [ABSTRACT FROM AUTHOR]

In recent years, the application of deep neural networks in the field of image classification has become increasingly widespread. However, existing researches have exposed the inherent vulnerabilities and opacity of these network models, rendering them susceptible to carefully crafted adversarial attacks. This poses significant security risks to their practical deployment. Given that the majority of models in real-world applications operate in a black-box manner, current research on black-box attacks targeting classification models remains inadequate, facing significant challenges such as incomplete robustness evaluations, low success rates of black-box attacks, and excessive resource consumption during the attack process. To address these challenges, this paper innovatively proposes a black-box attack algorithm based on a cycle-consistent generative network. This algorithm directly generates adversarial perturbations through a generative network that integrates an attention mechanism, deeply learning the complex relationship between adversarial samples and clean sample data. It supports both targeted and untargeted attack modes. Experimental validation on the SAT dataset demonstrates that our method achieves an average white-box attack success rate exceeding 96% in classification tasks and an average success rate of up to 69.4% in black-box transfer attacks, exhibiting excellent transferability across different models. This research contributes significantly to enhancing the effectiveness of black-box attacks, reducing attack costs, and providing novel ideas and methods for enhancing the security of deep learning models. • We proposed an optimized cycle-consistent generative network for adversarial attacks, ultimately enhancing the success rate against other diverse black-box models. • Our algorithm offers support for both targeted and non-targeted attacks. Our targeted approach confers directionality to the generated perturbations, enabling purposeful attacks tailored to the attacker's intentions. • A transformation distribution module is incorporated into our model. We introduce a distance loss function to our overall loss computation. • Our approach enables migration attacks across different models, achieving a black-box attack success rate superior to current methods, exceeding 69.4%. [ABSTRACT FROM AUTHOR]

Interpretation of the prediction is vital for mission critical tasks. Accurate interpretation relies upon the generalization accuracy of the model. In this paper, we propose a modified gradient descent learning algorithm to improve the generalization ability of a Spiking Neural Network with time-varying weights (SNN-t). This algorithm is referred to as GradST, can help towards improving the interpretation of multiclass classification problems. We have transformed the SNN-t to a Generalized Additive Model (GAM) to provide interpretation. The resultant Spiking Additive Model (SAM) has the generalization ability of SNN-t and the interpretable characteristics of GAM for multiclass problems. We also propose a post-processing method to enable better visualization of multiple shape functions of GAMs, towards better relative interpretation for multiclass classification problems. The post-processing method utilizes the properties of multiclass GAMs to visually modify the shape functions to establish the importance of the feature in multiclass setting. We first evaluate the performance of SNN-t, trained with GradST and the SAM generated from it, on large public datasets. The SNN-t trained with GradST has better generalization accuracy than other SNN-t classifier and consequently, the SAM generated from it has better generalization accuracy than other state-of-the-art multiclass GAMs. Improved accuracy in SAM implies more reliable interpretation. Then, we evaluate the proposed post-processing method for multiclass GAMs to provide relative interpretation. It is observed that relative interpretation of multiclass GAM is more meaningful and reliable. • Modified gradient descent-based learning algorithm for a spiking neural classifier. • Transformation of spiking neural classifier to an interpretable classifier. • Improve generalization ability for reliable interpretation. • Post-processing method for relative interpretation. [ABSTRACT FROM AUTHOR]

In recent decades, bio-inspired numerical algorithms have emerged as an alternative for optimizing the structural design of trusses. The differential evolution algorithm (DEA) has demonstrated both good performance and ease of implementation. However, unlocking the full potential of DEA to address engineering problems poses a significant challenge, necessitating a strategic and informed definition of each component of the algorithm. This research systematically evaluates the influence of defining DEA components on improving the reliability of truss optimization. The algorithm structure of DEA was configured for five aspects: (I) the mutation operator, (II) inclusion of multi-modal techniques, (III) inclusion of parameter control techniques, (IV) definition of the initial population, and (V) local search heuristics. A comprehensive evaluation is conducted to assess the performance of 23 DEA configurations in optimizing eight planar and spatial trusses, varying in size from 10 to 163 elements. Assessment is based on key criteria such as optimal weight, robustness, and computational cost, providing a thorough basis for comparison. The results showed that no tested DEA configuration is the best for all trusses. Instead, the study revealed the presence of recommendable configurations, each tailored to the specific complexities and scales inherent in various truss structures. The integration of multimodal and local search techniques proves particularly advantageous for larger trusses, amplifying the algorithm's exploratory capabilities to effectively navigate and uncover optimal regions. In contrast, using parameter control technique was more effective in optimizing smaller trusses, capitalizing on the rapid exploration of potential optimal areas in a smaller search space. There was a mutation operator that produced the best results for large trusses and good results for smaller structures. This operator uses the target vector as the base vector and guides its movement from a best-based difference vector, achieving a balance between the exploratory stage and user-defined constraints that promote the exploitation of potentially optimal areas. No discernible impact was observed when the initial population heuristic proposed was used. Finally, this study underscores the feasibility of DEA configurations for optimizing trusses of varying complexities, as proved by a comparison with results from the literature. • Mutation and multimodal techniques impact the DEA performance on truss optimization. • Custom insights refine the optimal design of trusses at varying levels of complexity. • DEA configurations yield improved truss designs as confirmed by literature results. • Paper links DEA concepts to truss optimization, aiding understanding. [ABSTRACT FROM AUTHOR]

Topological data analysis (TDA) is a powerful mathematical framework that extracts valuable insights about the shape and structure of complex datasets by identifying and analyzing underlying topological features, including connected components, holes, voids, and higher-dimensional counterparts. TDA achieves this by constructing a simplicial complex from the data, comprising simple shapes like points, lines, triangles, and higher-dimensional counterparts. The Vietoris–Rips complex (VRC), a widely used method, constructs simplicial complexes by measuring the distances between data points in a metric space. While the link between Intuitionistic Fuzzy Sets (IFSs) and TDA is clear through intuitionistic fuzzy distance measures, the literature has not explored the application of TDA for extracting topological features from uncertain and vague datasets using IFSs. In this paper, we propose a novel approach that leverages Intuitionistic Fuzzy Distance Measures (IFDMs) to generate the Intuitionistic Fuzzy Vietoris–Rips Complex (IFVRC) of IFSs. Specifically, we investigate the persistence of invariant relations of topological features between IFVRCs constructed using different distances, including Atanassov's Euclidean distance (l 2 I F S), Boran and Akay's distance (D), and Liu's distance measure (d L), over artificially generated IFSs. Furthermore, we demonstrate that the generation of IFVRC can be viewed as a Natural Hierarchical Clustering Algorithm (NHCA) for IFSs. Finally, we apply the proposed IFVRC as a Natural Hierarchical Clustering Algorithm to cluster intuitionistic fuzzy datasets related to Car and Building materials. Moreover, we incorporate a relatively large dataset, the GTZAN dataset from the Kaggle datasets repository, to classify music genres based on their topological information. • Topological analysis of intuitionistic fuzzy distance measures (IFDMs) have been conducted. • Utilizing IFDMs, Intuition Fuzzy Vietoris Rips Complex (IFVRC) generated. • It is shown how to extract topological features from IFVRC. • The persistence relations of topological features between IFVRCs have been investigated. • Finally, IFVRC is treated as a natural hierarchical clustering algorithm. [ABSTRACT FROM AUTHOR]

Reinforcement Learning (RL) has gained significant popularity in recent years for its ability to solve complex control problems. However, most existing RL algorithms are designed to train policies for each environment in isolation, limiting their applicability to real-world scenarios with many related environments. Recently, many multitask optimization algorithms have been proposed and successfully applied to a wide range of optimization problems. However, existing studies mainly focus on solving multiple continuous function benchmarks and overlook the potential of tailoring towards RL. In this paper, we propose a simple multitask optimization algorithm called Multitask Augmented Random Search (MARS) that trains multiple RL agents together and exploits the performance surplus from highly correlated tasks. MARS is a modification of the simple random search Augmented Random Search (ARS) algorithm, which has been shown to outperform complicated methods in solving continuous control MuJoCo environments such as Soft Actor-Critic (SAC), Proximal Policy Optimization (PPO), and Trusted Region Policy Optimization (TRPO). The experimental results also demonstrate that our proposed algorithm is more consistent in solving different instances of MuJoCo benchmark than ARS, Multifactorial Evolution Algorithm (MFEA), and Adaptive MFEA RL (AMFEARL) within the same number of training episodes. • Propose a multitask extension of ARS for training related continuous control problems. • Derived an online data-driven measurement to preempt the transfer of solutions incomplementary. • Conduct experiments on synthetic benchmarks and in the multitask Mujoco environments. [ABSTRACT FROM AUTHOR]

Instance selection is an important preprocessing technology in data mining and machine learning. In this paper, we proposed a novel evolutionary based instance selection algorithm for big data. First, we defined a coarse granularity chromosome structure to reduce the size of search space and costs of chromosome operations(recombination and mutation, etc.). Then a stratified evolution strategy was proposed to remove the hyper parameter in classic fitness function and achieve precise control over the reduction ratio of instances. Finally, a sampling-based fitness function was proposed to reduce the time complexity. Experimental results shown that our new algorithm is efficient to complete the instance selection task on data set with millions of instances in minutes-level. The 10-fold cross-validation also proved that the selection results on many datasets have high nearest neighbor classification accuracy. • We proposed a distributed coarse granularity chromosome structure to reduce search space and ensure load balance. • We proposed a stratified evolution strategy to transform multi-objective to multiple single-objective tasks. • We proposed a sampling-based fitness function with less time complexity [ABSTRACT FROM AUTHOR]

This paper introduces a novel meta-heuristic optimization algorithm named Clustering Wavelet Opposition-based Marine Predators Algorithm (CWOMPA) to address some limitations present in the well-established Marine Predators Algorithm (MPA). CWOMPA incorporates three key strategies: a fuzzy clustering approach for escaping local optima, using wavelet basis function-based impact coefficient adjustment for elites to prevent premature convergence, and finally an adaptive opposition-based learning strategy for maintaining population diversity. Compared with some recent meta-heuristic algorithms, extensive evaluations conducted affirm that CWOMPA achieves the best Friedman rank, 4.30 and 1.95 respectively, on 23 benchmark functions and the CEC 2017 benchmark set. Not only does CWOMPA demonstrate significant effectiveness on six constrained problems from the CEC 2020 real-world benchmarks, but also when applied to 14 medical datasets, it gains superior Friedman ranks in terms of selected features, classification accuracy, F-Score, and objective function value, 2.5, 2.25, 2.96, 2.93 respectively, outperforming other common methods. Finally, CWOMPA exhibits the highest classification accuracy across all datasets and the best F-Score performance in nine datasets compared to traditional feature selection algorithms. The obtained results reveal that CWOMPA is a powerful and versatile optimization algorithm with significant potential in various real-world applications, including feature selection for medical datasets. [Display omitted] • An improved MPA called CWOMPA is proposed to improve the optimization performance. • CWOMPA incorporates FCM, wavelet basis function, and AOBL to enhance the MPA. • CWOMPA can be an effective tool in addressing feature selection challenges. [ABSTRACT FROM AUTHOR]

In recent years, there have been notable advancements in the theoretical research of existing meta-heuristic algorithms and in the development of novel algorithms. However, the research on the definition and construction methods of the so-called "mutation" operator, a concept widely used in existing meta-heuristic algorithms, remains notably insufficient. In addition, how to directly construct a meta-heuristic algorithm under the guidance of stability and invariance analysis continues to pose a challenge that has yet to resolve. This paper carefully investigates and rigorously defines the concepts of "mutation operator" and "mutation-driven meta-heuristic algorithm" as found in the literature. According to these definitions, a novel meta-heuristic algorithm, called Multi-direction guided Mutation-driven Stable swarm Intelligence algorithm with Translation and Rotation invariance (M2SITRI) is proposed, and the corresponding mutation operator is carefully designed based on the analysis of translation invariance, rotation invariance, order-1 stability, and order-2 stability. The stability and invariance properties of the M2SITRI algorithm are validated through numerical simulations, and its superior solving performance is demonstrated by benchmarking it against several other well-known existing meta-heuristic algorithms across a variety of test problems. • Mutation operator and mutation-driven meta-heuristic algorithm concepts are defined. • A novel translation and rotationally invariant meta-heuristic algorithm termed M2SITRI is proposed. • The invariance and stability properties and the excellent solving performance of M2SITRI algorithm are verified. [ABSTRACT FROM AUTHOR]

Abstract Regression problems try estimating a continuous variable from a number of characteristics or predictors. Several proposals have been made for regression models based on the use of fuzzy rules; however, all these proposals make use of rule models in which the irrelevance of the input variables in relation to the variable to be approximated is not taken into account. Regression problems share with the ordinal classification the existence of an explicit relationship of order between the values of the variable to be predicted. In a recent paper, the authors have proposed an ordinal classification algorithm that takes into account the detection of the irrelevance of input variables. This algorithm extracts a set of fuzzy rules from an example set, using as the basic model a sequential covering strategy along with a genetic algorithm. In this paper, a proposal for a regression algorithm based on this ordinal classification algorithm is presented. The proposed model can be interpreted as a multiclassifier and multilevel system that learns at each stage using the knowledge gained in previous stages. Due to similarities between regression and ordinal problems as well as the use of a set of ordinal algorithms, an error interval can be returned with the regression output value. Experimental results show the good behavior of the proposal as well as the results of the error interval. Highlights • We present a new fuzzy rule learning algorithm for regression problems. • The algorithm is based on an ordinal one which uses the sequential covering strategy with a genetic algorithm as the search mechanism. • The final regression algorithm is a multiclassifier and multilevel system which learns in an incremental way by using the learnt knowledge in previous stages. • An error interval can be returned with the regression output value. • The results show a good behavior compared with the same category algorithms, so as, other category algorithms, for regression problems. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are currently being studied in depth by many scholars, and it is an important task to improve the learning and adaptive capabilities of the algorithms so that they can be of great use in a wide range of optimization problems. The Manta ray foraging optimization (MRFO) has made certain achievements in optimization problems, but it still has shortcomings, such as its insufficient intra-population communication and poor learning ability, so the optimization capability of MRFO needs further improvement. To address these shortcomings, this paper proposes a manta ray foraging optimization based on mechanics game and progressive learning for multiple optimization problems, which is abbreviated as MGL-MRFO, introducing variable spiral factors and dynamic adjustment of internal parameters; then proposes progressive learning to enhance the learning ability of MRFO; and finally proposes a matching game-based update mechanism to eliminate intra-population repetition as a feasible solution. It is validated in terms of move step size and time complexity. The results are compared with variants of the algorithm proposed in recent years in 18 benchmark test functions and the CEC 2022 test set, as well as with the TOP algorithm in the CEC competition, and show that MGL-MRFO has strong advantages, validating the novelty and competitiveness of MGL-MRFO. At the end of the paper, the feasibility and practicality of MGL-MRFO is further validated by three engineering optimization problems. [Display omitted] • Analyze MRFO and propose corresponding strategies. • Designed progressive learning and optimal position update based on matching game. • The proposed algorithm was tested in 18 benchmark functions and CEC 2022. • The practicality of the algorithm is verified by three engineering optimization problems. [ABSTRACT FROM AUTHOR]

Being sensitive to initial position and noise is the key problem that must be solved in point cloud registration. To improve the overall performance of registration methods, a point cloud registration method based on the Dark Forest Algorithm is proposed in this paper. The introduction of swarm intelligence enhances the adaptability of the registration method to the initial position and improves the robustness of the point cloud registration system. Furthermore, the application of the Dark Forest Algorithm with four capability enhancement strategies: the hierarchical elite, civilization self-decision, suspicion chain, and technology explosion has improved the comprehensive competitiveness in point cloud registration. The mentioned strategies have brought a great positive impact on the registration methods in solving the far initial position, breaking away from the local optimal solution, and weakening the noise interference. The paper aims to provide the Dark Forest Algorithm and the point cloud registration method based on it, which are elaborated on in detail and compared to other existing algorithms to prove the comprehensive performance, respectively. Then, the point cloud registration experiment for coal-wall point clouds was carried out and the results indicate that the proposed registration algorithm is better than other algorithms for standard point cloud dataset and coal-wall point cloud, which demonstrates that the proposed registration method has strong potential competitiveness. • Proposed a point cloud registration based on a novel meta-heuristic algorithm. • Dark Forest Algorithm is proposed with the higher accuracy and stronger adaptability. • Cloud registration based on the DFA demonstrates potential competitiveness. [ABSTRACT FROM AUTHOR]

In response to the low-carbon development strategy, the new energy vehicle has become one crucial and recognized tool to satisfy the emission reduction requirements, the representative advance of the new energy vehicle is its intelligent drive technique. This paper introduces the driver's subjective guidance to further improve the intelligent drive technique. To do this, the hesitant convolutional neural networks (HCNN) are proposed to deal with the above issue, in which the hesitant fuzzy set is used to fully describe the subjective guidance information. Thus, the different driver's personalized needs can be considered and fused into the intelligent drive algorithm to achieve the intelligent drive technique of new energy vehicles. Note that the innovation and difference of the proposed method are the subjective guidance fusion and its generalized presentation but not the intelligent drive calculation. After that, the data downward partition model and weight secondary matching calculation are developed based on the HCNN to fully describe and process the hesitant fuzzy subjective guidance information. Furthermore, the intelligent drive algorithm considering the driver's subjective guidance is provided according to the above methods. Finally, an illustrative example is given to show the effectiveness of the proposed algorithm in the given drive scenario. • This paper improves intelligent drive technique by introducing driver's subjective guidance. • The hesitant fuzzy set is used to fully describe the driver's subjective guidance. • The hesitant CNN (HCNN) is proposed to achieve the above intelligent drive technique. • We further give the corresponding downward partition and secondary matching models. • The intelligent drive algorithm based on the new ideas and models is designed and demonstrated. [ABSTRACT FROM AUTHOR]

Multi-cloud environment enables an organization to access services from more than one cloud service providers the use of multiple cloud computing and it can be treated as single heterogeneous environment. It enables autonomy to run the tasks on private or public cloud based on business or technical requirements. In a multi-cloud platform, load balancing is an essential task to serve the requests from multiple users with different resources effectively. It helps to improve utilization of the cloud resources, throughput, reduce makespan and avoid overload at resources. Load balancing also facilitates the redirection of traffic to resources running in another cloud when a failure occurs in a cloud. Hence, it is more vital to have optimized load balancing methods in multi-cloud infrastructure in order to improve the system performance. This paper presents an optimized fault tolerant load balancing method using multi-objective cat swarm optimization algorithm called MCSOFLB and the results are then compared against other powerful optimization algorithms. The experimental results evidently show that the proposed algorithm ranks first on the whole. The MCSOFLB method produces an average improvement of 31 % makespan, 6 % resource utilization, 12 % cost, 6 % success rate and 32 % average throughput over other benchmark algorithms. • Multi-objective CSO. • Fault tolerance. • Load balancing. • Task scheduling. • High resource utilization. [ABSTRACT FROM AUTHOR]

Electric vehicle routing problem (EVRP) is a special vehicle routing problem, which owns several specific characteristics of EV technology including the limited charging stations and the limited cruising range of EVs. This paper proposes a two-stage heuristic algorithm for EVRP with a kind of pseudo node, termed EVRPPN-TSH, which not only contributes to the model innovation but also to the algorithm design. In model innovation, the pseudo node is introduced into the EVRP model, forming EVRPPN, to reduce the search space and time complexity for obtaining service orders of customers from O (n) to O (1). Moreover, different from other research works which only use total distance as the fitness function, an improved fitness function is proposed, which takes all the distance, the electricity and capacity constraints compliance into consideration. In algorithm design, we first adopt the two-stage idea, which divides EVRP into the capacitated vehicle routing problem (CVRP) and a fixed route vehicle charging problem (FRVCP). Then we design a two-stage heuristic algorithm, termed as TSH. Specifically, for CVRP, a route division heuristic algorithm and a depot move heuristic algorithm are designed to divide routes. Further, an adjustment strategy with three operators is designed for adjusting the customer order. For FRVCP, an existing heuristic algorithm is adopted and enhanced by a variable extent shaking algorithm to avoid local trapped. Experimental results show that EVRPPN-TSH generally outperforms other state-of-the-art algorithms. Moreover, EVRPPN-TSH can obtain the best-known solution in all the 7 test cases, and update the best-known solution in test case E101 from 834.84 to 834.22. • In model innovation, we introduce pseudo node in EVRP model to reduce complexity. • An improved fitness function which considers constraint compliance is proposed. • In algorithm design, we propose two-stage heuristic approach to improve performance. • Illustrate the superiority of the improved model and the proposed algorithm. [ABSTRACT FROM AUTHOR]

In practical applications, Bayesian classification methods have been successfully employed. The Naïve Bayes algorithm (NB) is a quick, successful, and well-known Bayesian classification method. The Naïve Credal Classifier (NCC) is a version of NB that outputs imprecise predictions (sets of class values). NCC was also adapted for considering classification error costs. Such an adaptation is the only Bayesian method for Imprecise Classification proposed so far that considers misclassification costs. This paper presents a Bayesian algorithm for Imprecise Classification that weights the instances using the misclassification costs in such a way that the importance of an instance increases as the error cost of its class value is higher. We highlight that our proposal may provide more informative and intuitive outcomes than the existing cost-sensitive NCC. We experimentally show that our new proposed method improves the existing cost-sensitive NCC. Moreover, we highlight that our imprecise classifier has a processing time equivalent to the original NB algorithm for precise classification, which has been successfully applied to very large and real datasets. This is a crucial point in favor of our proposal because of the huge amount of data in many application areas nowadays. • Imprecise classification can provide multiple responses. • Imprecise classifiers are useful with insufficient evidence for a precise output. • Bayesian methods have been sucessfully used in practical applications. • Classification errors are very important in practical applications. • A successful Bayesian method for cost-sensitive Imprecise Classification is proposed. [ABSTRACT FROM AUTHOR]

Attributed graph clustering, which aims to learn embedding representation and divides nodes into different groups, has attracted increasing attention in recent years. Existing investigations have demonstrated that graph attention network (GAT) exploiting graph structure and node attributes for clustering can yield remarkable performance. However, existing GAT-based algorithms usually use adjacency matrix or feature matrix directly, neglecting the processing of noise within the feature matrix. Furthermore, some methods fail to effectively fuse different levels of embedding information for the specific clustering task. To address these deficiencies, we propose a multi-embedding fusion network for attributed graph clustering (MEFGC for short) in this paper. Specifically, in our model, a novel Laplacian filter is first designed to alleviate high-frequency noise. Secondly, we design a multi-embedding fusion module, which includes an improved auto-encoder and graph attention network, to obtain superior node embedding representation. Finally, a reliable target distribution generation method is designed, utilizing a joint supervision strategy combining self-supervision and mutual supervision to optimize the node embedding. Extensive experiments on four benchmark datasets demonstrate that the proposed MEFGC achieves state-of-the-art results in clustering tasks. • A GCN-based parameterless Laplacian filter removes noise, enhancing clustering. • A multi-embedding fusion module generates more representative embedding representation. • Joint self and mutual supervision improves clustering performance. [ABSTRACT FROM AUTHOR]

This paper presents a causal discovery model for time series analysis, which can find several causal lags of one variable over another, presenting the results by means of a temporal causal graph. The proposed model is based on multi-head attention neural networks and includes convolutional networks, allowing high prediction performance and the ability to estimate confounders. The developed framework presents an innovative methodology for validating potential causes: through a second neural network model, a direct and inverse model is trained with the potential causal lag between the causal variable under study and the target variable. In addition, another potential causal discovery method based on a non-linear correlation test is implemented. The main results show that the proposed model outperforms other causal algorithms concerning the F1 Score and achieves a considerable performance in finding temporal lags. In addition, the network fit (evaluated through the mean squared error) is significantly improved when only the causal variables predicted by the proposed algorithm (with their corresponding causal lags) are used for model training, relative to the incorporation of the entire time series dataset for the prediction of the target variable. • Causal discovery model able to find several causal lags of one variable over another. • Proposed model is based on multi-head attention and convolutional networks. • Proposed model allows high prediction performance and finding confounders. • The framework presents methodology for validating potential causes. [ABSTRACT FROM AUTHOR]

The characteristic of multi-modal multi-objective optimization problems (MMOPs) is that multiple equivalent Pareto solution sets (PSs) in the decision space correspond to the same Pareto front (PF) in the objective space. The difficulty in solving the MMOPs lies in how to maintain the distribution in space. Many multi-modal multi-objective evolutionary algorithms (MMEAs) take convergence as the primary selection criterion, which makes it difficult for the algorithm to find all PSs in the decision space. In view of this situation, this paper proposes a partitioned knowledge-guided MMEA with multi-stage. The algorithm makes stage changes according to the proportion of evaluation consumed by the algorithm during the evolution, and adjusts the environment selection strategy as the stage changes. At the beginning of evolution, region division is carried out to prevent the solutions on each PS from interfering with each other and evolving independently. When the evaluation consumption reaches a certain proportion, it enters the middle stage. The information of the obtained solutions are used to guide the evolutionary direction of the population, and the deleted promising solutions are reclaimed. In the later stage, the steady state updating is performed to improve the distribution of population. The experimental results on four multi-modal multi-objective test suites with different features show that the proposed algorithm is more competitive than other seven excellent algorithms. • The regional division strategy makes the population move closer to potential PSs. • The solution reclaim strategy reclaims deleted promising solutions. • The environment selection makes stage changes by proportion of evaluation consumed. [ABSTRACT FROM AUTHOR]

Feature selection is a crucial data preprocessing technique extensively employed in machine learning and image processing. However, feature selection encounters significant challenges when addressing high-dimensional data due to the huge and discrete decision space. This paper proposes a hierarchical learning multi-objective firefly algorithm (HMOFA) for solving the feature selection task in high-dimensional data. The main contributions are as follows: 1) Features are clustered based on the evaluation of multiple metrics, which are used to initialize the population and improve the quality of the initial population; 2) A hierarchy-guided learning model is proposed, where individuals move toward superior solutions while moving away from inferior solutions, avoiding the oscillation phenomenon that occurs under the full attraction model, and reducing the likelihood of the population being trapped in a local optimum; 3) Use duplicate solution modification mechanism to reduce the number of duplicate individuals in the population. The proposed method is compared with 8 competitive feature selection methods using 15 datasets, and the results demonstrate that HMOFA can achieve higher classification accuracy while selecting fewer features. • HMOFA is proposed to solve high-dimensional feature selection tasks. • A clustering initialization method is introduced to reduce redundant features and improve the quality of initial population. • The population updates its position using a hierarchy-guided learning model. • A duplicate solution modification mechanism is applied to enhance the diversity of the population. [ABSTRACT FROM AUTHOR]

Dynamic multiobjective optimization problems (DMOPs) typically involve multiple conflicting time-varying objectives that require optimization algorithms to quickly track the changing Pareto-optimal front (POF). To this end, several methods have been developed to predict new locations of moving Pareto-optimal solution set (POS) so that populations can be re-initialized around the predicted locations. In this paper, a dynamic multi-objective optimization algorithm based on a multi-directional difference model (MOEA/D-MDDM) is proposed. The multi-directional difference model predicts the initial population through the estimated populations developed by a designed POS estimation strategy. An adaptive crossover-rate approach is incorporated into the optimization process to cope with different POS structures. To investigate the performance of the proposed approach, MOEA/D-MDDM has been compared with six state-of-the-art dynamic multiobjective optimization evolutionary algorithms (DMOEAs) on 19 benchmark problems. The experimental results demonstrate that the proposed algorithm can effectively deal with DMOPs whose POS has a single-modality characteristic and continuous manifolds. • Dynamic multiobjective optimization evolutionary algorithm is designed. • The multi-directional difference model is applied to predict the initial population. • POS estimation strategy improves the quality of historically obtained solutions. • An adaptive crossover rate strategy is designed. • Systematic studies demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Multi-view clustering has achieved many applications in recent years. But the existing multi-view clustering methods face two problems, firstly, the traditional multi-view clustering uses a hard clustering method, which cannot describe the uncertainty between the samples and the clusters, and secondly how to perform effective incremental learning on multi-view data when the number of views increases. Therefore to address the above two problems, we firstly propose a three-way fuzzy spectral clustering algorithm based on fuzzy clustering, which can perform three-way clustering on multi-views and get soft clustering results, thus describing the uncertainty between samples and clusters. Then based on the sequential decision making approach, an incremental learning mechanism is designed for multi-view clustering when the number of views increases. Finally, these two works are combined to propose a multi-view clustering algorithm based on sequential three-way decision making. The experimental results demonstrate that the method proposed in this paper has better clustering accuracy and efficiency compared to the traditional multi-view clustering algorithm. • As incremental data comes in, the performance of the strategy goes up. • Methods can delay decisions when there is insufficient data for multiple views. • Methods are suitable for saving the results obtained by strategies when there is not enough information. • Strategies can be more time efficient than traditional methods. [ABSTRACT FROM AUTHOR]

The field of machine learning (ML) has witnessed significant advancements in recent years. However, many existing algorithms lack interpretability and struggle with high-dimensional and imbalanced data. This paper proposes SPINEX, a novel similarity-based interpretable neighbor exploration algorithm designed to address these limitations. This algorithm combines ensemble learning and feature interaction analysis to achieve accurate predictions and meaningful insights by quantifying each feature's contribution to predictions and identifying interactions between features, thereby enhancing the interpretability of the algorithm. To evaluate the performance of SPINEX, extensive experiments on 59 synthetic and real datasets were conducted for both regression and classification tasks. The results demonstrate that SPINEX achieves comparative performance and, in some scenarios, may outperform commonly adopted ML algorithms. The same findings demonstrate the effectiveness and competitiveness of SPINEX, making it a promising approach for various real-world applications. • SPINEX: An interpretable neighbor exploration algorithm is presented. • Combines ensemble learning and feature interaction for meaningful insights. • Quantifies feature contributions and interactions to enhance interpretability. • 59 experiments were conducted to validate SPINEX's comparative performance. [ABSTRACT FROM AUTHOR]

In knowledge discovery and data mining, removing redundant and irrelevant data attributes is crucial. Traditional algorithms, however, struggle with efficiency in high-dimensional big data contexts. To solve this problem, this paper proposes a novel global search attribute reduction method namely RHC-IGWO (Rough Hypercuboid Structure via Improved Grey Wolf Optimizer) by integrating the rough hypercuboid method and Improved Grey Wolf Optimizer (IGWO) with the pheromone mechanism. The algorithm is embedded into the Apache Spark parallel computing framework (Parallel computing Rough Hypercuboid Structure via Improved Grey Wolf Optimizer, PcRHC-IGWO) to accelerate and simplify the attribute reduction process. The algorithm divides the decision table into several independent blocks, introduces the pheromone mechanism to simulate the wolf pack behavior, and uses the IGWO for global search, which is conducive to efficient local search and global information sharing between individuals. The position of the individual is initialized by calculating the relevance between the attributes, and the pheromone value is dynamically updated according to the reduction quality. This allows automatically giving more search focus to more promising attribute regions. Experiments with public and real datasets demonstrate the RHC-IGWO algorithm's significant speedup and its efficacy in maintaining or enhancing classification accuracy. Particularly noteworthy is its performance on schizophrenia datasets, where the proposed method achieves outstanding classification accuracies of 86.2%, 88.89%, and 92.86% across various classifiers. These results not only demonstrate its effectiveness but also underline its potential in advanced data analysis scenarios. Additionally, on some large-scale datasets, the time required for processing has been reduced by 85.71%, showcasing the algorithm's efficiency in handling complex and voluminous data. • This study presents the unbalanced evaluation indicators of attribute subsets by parallel computing. • The pheromone and mutation mechanisms in BGWA are combined with rough hypercuboid approach. • This study presents a distributed meta-heuristic attribute reduction via Apache Spark. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) refers to the various devices connected to the Internet, enabling them to communicate and transmit data with each other. The rapid development of the IoT also brings security and other problems in cyberspace. In this case, device identification is a crucial tool for IoT security issues, which can detect and prevent cyber-attacks. However, device identification has some challenges on IoT traffic datasets, such as large-scale and high-dimensional sparse datasets, features prone to security vulnerabilities, and identification of IP and non-IP devices, which affects the performance of classifiers. Feature selection can be seen as an effective data preprocessing technique in IoT device identification, which may improve the performance of classification and reduce the computational complexity of IoT device identification. In this paper, we propose a traffic-based IoT device identification model using a novel wrapper feature selection approach based on an improved and efficient method, which we call Lévy flight-based sine chaotic sub-swarm binary honey badger algorithm (LS2-BHBA). Specifically, four improved factors are employed in LS2-BHBA to expand the search scope, balance the exploration and exploitation phases, and enhance the search capability. In addition, a binary mechanism is implemented to enhance the suitability of the proposed LS2-BHBA for feature selection in IoT device identification. The experimental results on several real IoT traffic datasets denote that LS2-BHBA can reduce the number of features to 10%, and the classification accuracy can reach 98%, which outperforms some classical and latest comparison algorithms in the feature selection of IoT device identification. • A traffic-based IoT device identification model including wrapper feature selection is proposed. • An improved binary honey badger algorithm for IoT traffic datasets (denoted as LS2-BHBA) is designed. • A wrapper feature selection method based on LS2-BHBA is designed. • Extensive experiments are conducted on three real-world datasets to demonstrate the effectiveness and generalization of the proposed method. • The results are tested statistically and the feasibility of using decomposition methods is analyzed. [ABSTRACT FROM AUTHOR]

Signal recovery problem in compressed sensing can be mathematically modeled as a ℓ 0 regularized problem, which aims at searching a sparse solution. When tackling this problem, traditional mathematical approaches suffer from a limited convergence ability, especially under the noisy condition. To better solve this problem, in this paper, a novel differential evolutionary (DE) algorithm is designed to combine with a local search approach. First, an adaptive control strategy for DE is extended to recover sparse signals with noise in this paper, which is found to have a promising recovery performance. Second, in order to further enhance the convergence speed, a local search approach, i.e. , a shrinkage-thresholding method (STM), is embedded into the evolutionary process of DE. Therefore, the advantages of local search capability provided by STM and global search ability of DE can be effectively combined, and resultantly a novel local search enhanced adaptive DE (named LSE-ADE) algorithm is proposed. Experimental results validate that LSE-ADE performs better than the eight classic sparse recovery algorithms and one recently proposed evolutionary algorithm, when recovering sparse signal under the noisy condition. [ABSTRACT FROM AUTHOR]

The main goal of this paper is to study the performance of the Grey Wolf Optimizer (GWO) algorithm when a new hierarchical operator is introduced in the algorithm. This new operator is basically a hierarchical transformation that is inspired in the hierarchical social pyramid of the grey wolf. This proposed operator is applied to the simulation of the hunting process in the algorithm and has 5 variants that are explained in more detail in this paper (centroid, weighted, based on the fitness and two variants using fuzzy logic). Notably the variants having the greatest impact in the GWO performance are based on the use of fuzzy logic. We also present the motivation and results of experiments, as well as the benchmark functions that were used for the tests that are presented. In addition we are presenting a comparison among all methods for 30, 64 and 128 dimensions and we conclude that the performance of the Hierarchical GWO algorithm is better when using a fuzzy variant of the hierarchical operator. [ABSTRACT FROM AUTHOR]

This paper presents a runner-root algorithm (RRA) for electric distribution network reconfiguration (NR) problem. The considered NR problem in this paper is to minimize real power loss, load balancing among the branches, load balancing among the feeders as well as number of switching operations and node voltage deviation using max-min method for selection of the final compromised solution. RRA is equipped with two explorative tools, which are random jumps with large steps and re-initialization strategy to escape from local optimal. Moreover, RRA is also equipped with an exploitative tool to search around the current best solution with large and small steps to ensure the obtained result of global optimization. The effectiveness of the applied RRA in both single- and multi-objective has been tested on 33-node and 70-node distribution network systems and the obtained test results have been compared to those from other methods in the literature. The simulation results show that the applied RRA can be an efficient method for network reconfiguration problems with single- and multi-objective. [ABSTRACT FROM AUTHOR]

In this paper, the Periodic Capacitated Arc Routing Problem (PCARP) is investigated. PCARP is an extension of the well-known CARP from a single period to a multi-period horizon. In PCARP, two objectives are to be minimized. One is the number of required vehicles ( nv ), and the other is the total cost ( tc ). Due to the multi-period nature, given the same graph or road network, PCARP can have a much larger solution space than the single-period CARP counterpart. Furthermore, PCARP consists of an additional allocation sub-problem (of the days to serve the arcs), which is interdependent with the routing sub-problem. Although some attempts have been made for solving PCARP, more investigations are yet to be done to further improve their performance especially on large-scale problem instances. It has been shown that optimizing nv and tc separately (hierarchically) is a good way of dealing with the two objectives. In this paper, we further improve this strategy and propose a new Route Decomposition (RD) operator thereby. Then, the RD operator is integrated into a Memetic Algorithm (MA) framework for PCARP, in which novel crossover and local search operators are designed accordingly. In addition, to improve the search efficiency, a hybridized initialization is employed to generate an initial population consisting of both heuristic and random individuals. The MA with RD (MARD) was evaluated and compared with the state-of-the-art approaches on two benchmark sets of PCARP instances and a large data set which is based on a real-world road network. The experimental results suggest that MARD outperforms the compared state-of-the-art algorithms, and improves most of the best-known solutions. The advantage of MARD becomes more obvious when the problem size increases. Thus, MARD is particularly effective in solving large-scale PCARP instances. Moreover, the efficacy of the proposed RD operator in MARD has been empirically verified. [ABSTRACT FROM AUTHOR]

Geotechnical reliability analysis provides a novel way to rationally take the underlying geotechnical uncertainties into account and evaluate the stability of geotechnical structures by failure probability (or equivalently, reliability index) from a probabilistic perspective, which has gained great attention in the past few decades. With the rapid development of artificial intelligence techniques, various machine learning (ML) algorithms have been successfully applied in geotechnical reliability analysis and the number of relevant papers has been increasing at an accelerating pace. Although significant advances have been made in the past two decades, a systematic summary of this subject is still lacking. To better conclude current achievements and further shed light on future research, this paper aims to provide a state-of-the-art review of ML in geotechnical reliability analysis applications. Through reviewing the papers published in the period from 2002 to 2022 with the topic of applying ML in the reliability analysis of slopes, tunneling, and excavations, the pros and cons of the developed methods are explicitly tabulated. The great achievements that have been made are systematically summarized from two major aspects. In addition, the four potential challenges and prospective research possibilities underlying geotechnical reliability analysis are also outlined, including multisensor data fusion, time-variant reliability analysis, three-dimensional reliability analysis of practical cases, and ML model selection and optimization. • A state-of-the-art review of ML in geotechnical reliability analysis applications. • Several commonly used ML algorithms and some latest advanced ML methods are summarized. • The potential challenges and prospective research possibilities are outlined. [ABSTRACT FROM AUTHOR]