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This paper proposes a strengthened RIME algorithm to tackle continuous optimization problems. RIME is a newly proposed physical-based evolutionary algorithm (EA) inspired by the soft and hard rime growth process of rime-ice, which has a powerful exploitation ability. But in complex optimization problems, RIME will easily trap into local optima and the optimization will become stagnation. Noticing this issue, we introduce three techniques to the original RIME: (1) Latin hypercube sampling replaces the random generator as the initialization strategy, (2) modified hard rime search strategy, and (3) embedded distance-based selection mechanism. We evaluate our proposed SRIME in 10-D, 30-D, 50-D, and 100-D CEC2020 benchmark functions and eight real-world engineering optimization problems with nine state-of-the-art EAs. Experimental and statistical results show that the introduction of three techniques can significantly accelerate the optimization of the RIME algorithm, and SRIME is a competitive optimization technique in real-world applications. Ablation experiments are also provided to analyze our proposed three techniques independently, and the embedded distance-based selection contributes most to the improvement of SRIME. The source code of SRIME can be found in https://github.com/RuiZhong961230/SRIME. [ABSTRACT FROM AUTHOR]

Several optimization-based population search methods have been proposed; they use various operators that permit exploring the search space. These methods typically suffer from local search (LS) problems and are unbalanced between exploration and exploitation. Consequently, recent researchers sought to modify the algorithms to avoid search problems using local search techniques to intensify the exploitation when is necessary. This paper proposes a novel single-based local search optimization algorithm called the non-monopolize search (NO). The NO is a single-solution metaphor-free algorithm, and its operators are designed based to explore and exploit along the iterative process. The NO works only with a candidate solution, and the operators modify the dimension to move the current solution along the search space. The NO is an effective LS method that combines the benefits of exploration with exploitation. Different from other LS, the NO can escape from suboptimal solutions thanks to the randomness incorporated into its operators. This is the main advantage of the NO. Experiments are conducted on standard benchmark functions to validate the performance of the proposed non-monopolize search optimization technique. The results are compared with other well-known methods, and the proposed NO got better results. Moreover, the proposed NO can be considered a powerful alternative to improve the optimization algorithms' performance and help avoid local search problems. Source codes of NO are publicly available at https://www.mathworks.com/matlabcentral/fileexchange/156154-the-non-monopolize-search-no. [ABSTRACT FROM AUTHOR]

Using of ultra-wideband (UWB) technology in radio transceiver systems has increased in recent years due to high-speed data transmission, low power dissipation, low cost, and low complexity. In particular, distributed amplifier (DA) is a critical component of transceiver in UWB technology. However, designing an ultra-wideband DA with high performance becomes challenging. The DA design suffers from the tight trade-offs between the amplifier parameters such as gain, noise, linearity, input/output impedance matching, and power dissipation. In this paper, a new approach for multi-objective optimization of the DA is introduced. In the proposed approach, the meta-heuristic optimization techniques are applied over the entire bandwidth of the UWB, while the most recent optimization approaches for amplifiers are performed at the center frequency and they can't achieve the proper design specifications for wideband amplifiers. The simultaneous optimization of power gain (S21), noise figure (NF), input and output return loss (S11 and S22) are conducted over the wide bandwidth using three multi-objective optimization algorithms including Multi-Objective Inclined Planes System Optimization (MOIPO), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Particle Swarm Optimization (MOPSO). The obtained results demonstrate the tapered matrix DA optimized by MOIPO exhibits better performance than others. The circuit simulations are performed in 0.18 µm TSMC RF-CMOS technology. Simulation results show that the optimized tapered matrix DA by MOIPO, compared to NSGA-II and MOPSO, exhibits a good performance over the frequency band of 0.1–28 GHz with maximum S21 of 12.9 dB, NF less than 5.9 dB, S11 and S22 below than − 10 dB over the whole frequency band. The DC power dissipation is 25 mW from a 1.5 V supply. [ABSTRACT FROM AUTHOR]

This paper formulates multi-objective (MO) generation scheduling problem of an integrated energy system (IES). The integrated energy system comprises combined heat and power (CHP), hydroelectric, thermal and heat units. The interdependency of heat and power in CHP units, water transport delay among various multi-chain hydroelectric units and valve point loading effect of thermal units makes the generation scheduling problem a complex, constrained, discontinuous, non-differentiable and multimodal optimization problem. The main motive of this research work is to concurrently reduce the overall generation cost and pollutant emissions emitted by various generating units. The generation scheduling problem is treated as a MO problem owing to the contradictory nature of these objectives. Thus, a very proficient hybrid optimization approach, i.e., quantum-based cuckoo search algorithm (QCSA) with mutation operators, is proposed for searching for the optimal generation schedule of the MO-integrated energy system generation scheduling (IESGS) problem. The proposed technique significantly improves the solutions obtained by QCSA by utilizing three mutation operators, i.e., Cauchy, Gaussian, and opposition-based mutation. The proposed strategy has been implemented to MO-hydroelectric-thermal (HT) generation scheduling and MO-IESGS problems to demonstrate the efficacy of the proposed method. The cardinal priority method is utilized for finding the most suitable non-dominated solution for both problems. The obtained results are comparatively better than the published results. The proposed approach's robustness compared to the QCSA has been further verified using the t-test. Based on comparisons and statistical analysis, the proposed technique is a promising approach for handling complex, multi-dimensional and non-convex generation scheduling optimization problems. [ABSTRACT FROM AUTHOR]

The integration of demand response programs (DRPs) into the energy management (EM) system of microgrids (MGs) helps in improving the load characteristics by allowing consumers to interoperate for achieving techno-economic advantages. In this paper, an improved algorithm is called LINFO is proposed for modifying search ability of the original weIghted meaN oF vectOrs (INFO) algorithm as well as avoiding its weaknesses like trapping in a local optima. The improved algorithm's efficiency is confirmed by comparing its results with those obtained by the original INFO and other optimization techniques using different standard benchmark test functions. Moreover, this improved algorithm and the original version are applied for solving the EM problem with the aim of optimizing the operation cost of the MGs in the presence DRPs. They are used to solve day-ahead EM problem for optimal operation of renewable energy resources, the optimal generation from a conventional diesel engines (DEs); taking into account the participation of customers in DRP for minimizing MG operating cost, which includes the cost of DEs fuel and the power transactions cost with the main grid. To demonstrate the efficacy of the proposed LINFO, simulation results are compared with the results of well-known and newly developed optimization techniques. [ABSTRACT FROM AUTHOR]

Digital data plays a paramount part in various fields of life, in which multimedia data represent the majority of transferred digital data via communication networks. Consequently, cryptographers and researchers have paid attention to the security of multimedia. Chaotic models are utilised intensely with optimisation techniques for designing modern multimedia cryptosystems, but the role of optimisation approaches is to improve experimental outcomes rather than complex computations in designing cryptosystems. Amidst the rapid evolution of quantum resources, current multimedia cryptosystems may be cracked because their construction relies on mathematical paradigms. This paper introduces a robust image cryptosystem that leverages bit-level permutation, particle swarm optimization, the 3D logistic system, and quantum walk. In the presented cryptosystem, the probability distribution generated by quantum walk and the chaotic sequences produced by the 3D logistic function are utilized as inputs for a customized particle swarm optimization algorithm. The resulting keystreams are then employed in both the permutation phase (at the pixel and bit levels) and substitution phase of the image cryptosystem. Experimental results indicate that the suggested cryptosystem has high-security contra different raids such as differential, statistical, and occlusion attacks. The average outcome for local entropy, global entropy, NPCR (Number of Pixel Change Rate), UACI (Unified Average Changed Intensity), Chi-square, and encryption speed are measured at 7.9025, 7.99985, 99.61306%, 33.4657%, 256.960725, and 1.7081 megabits per second, respectively. [ABSTRACT FROM AUTHOR]

The accurate prediction of cardiovascular disease is an essential and challenging task to treat a patient efficiently before occurring a heart attack. In recent times, various intelligent healthcare frameworks have been designed with different machine learning and swarm optimization techniques for cardiovascular disease prediction. However, most of the existing strategies failed to achieve higher accuracy for cardiovascular disease prediction due to the lack of data-recognized techniques and proper prediction methodology. Motivated by the existing challenges, in this paper, we propose an intelligent healthcare framework for predicting cardiovascular heart disease based on Swarm-Artificial Neural Network (Swarm-ANN) strategy. Initially, the proposed Swarm-ANN strategy randomly generates predefined numbers of Neural Networks (NNs) for training and evaluating the framework based on their solution consistency. Additionally, the NN populations are trained by two stages of weight changes and their weight is adjusted by a newly designed heuristic formulation. Finally, the weight of the neurons is modified by sharing the global best weight with other neurons and predicts the accuracy of cardiovascular disease. The proposed Swarm-ANN strategy achieves 95.78% accuracy while predicting the cardiovascular disease of the patients from a benchmark dataset. The simulation results exhibit that the proposed Swarm-ANN strategy outperforms the standard learning techniques in terms of various performance matrices. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to present an improved neural network algorithm (INNA) for solving the reliability-redundancy allocation problem (RRAP) with nonlinear resource constraints. In this RRAP, both the component reliability and the redundancy allocation are to be considered simultaneously. Neural network algorithm (NNA) is one of the newest and efficient swarm optimization algorithms having a strong global search ability that is very adequate in solving different kinds of complex optimization problems. Despite its efficiency, NNA experiences poor exploitation, which causes slow convergence and also restricts its practical application of solving optimization problems. Considering this deficiency and to obtain a better balance between exploration and exploitation, searching procedure for NNA is reconstructed by implementing a new logarithmic spiral search operator and the searching strategy of the learner phase of teaching–learning-based optimization (TLBO) and an improved NNA has been developed in this paper. To demonstrate the performance of INNA, it is evaluated against seven well-known reliability optimization problems and finally compared with other existing meta-heuristics algorithms. Additionally, the INNA results are statistically investigated with the Wilcoxon sign-rank test and Multiple comparison test to show the significance of the results. Experimental results reveal that the proposed algorithm is highly competitive and performs better than previously developed algorithms in the literature. [ABSTRACT FROM AUTHOR]

This paper presents a multi-objective version of the artificial vultures optimization algorithm (AVOA) for a multi-objective optimization problem called a multi-objective AVOA (MOAVOA). The inspirational concept of the AVOA is based on African vultures' lifestyles. Archive, grid, and leader selection mechanisms are used for developing the MOAVOA. The proposed MOAVOA algorithm is tested oneight real-world engineering design problems and seventeen unconstrained and constrained mathematical optimization problems to investigates its appropriateness in estimating Pareto optimal solutions. Multi-objective particle swarm optimization, multi-objective ant lion optimization, multi-objective multi-verse optimization, multi-objective genetic algorithms, multi-objective salp swarm algorithm, and multi-objective grey wolf optimizer are compared with MOAVOA using generational distance, inverted generational distance, maximum spread, and spacing performance indicators. This paper demonstrates that MOAVOA is capable of outranking the other approaches. It is concluded that the proposed MOAVOA has merits in solving challenging multi-objective problems. [ABSTRACT FROM AUTHOR]

Fuzzy systems are intensively investigated and extended to construct forecasting models. In particular, intuitionistic fuzzy sets are used to capture higher levels of uncertainty occurring in the modeled data. Neural networks are also used to reflect nonlinearity relationships frequently observed in time series. This paper proposes a new hybrid system merging fuzzy system with neural networks and an advanced optimization technique, the principle of justified granularity. Using this technique, we construct an innovative time-series forecasting model. In the experimental part of the paper, we demonstrate the advantages arising from applying the proposed approach to metal price forecasting. Finally, we provide evidence that the proposed model is competitive with the current state-of-the-art models for the forecasting horizons of one and five days. [ABSTRACT FROM AUTHOR]

Modeling of mean monthly flow is of particular importance for long-term planning of processes relying on water abstraction, such as reservoir operations. Advantage of data-driven models for these applications is the ability to predict monthly streamflow based on the combined hydrological and climatological input data. Methodology and recommendations for implementation of supervised learning (SL), from choice of input variables and optimization of model parameters in dependence of dataset size to final model evaluation, remains generally undefined. The main objective of this paper is to model mean monthly flow by SL models, while optimization algorithms (genetic algorithm-GA and simulated annealing-SA) are used to optimize and automate the choice of parameters and the reliable set of input variables. Detailed analysis of accuracy and amount of time needed to build three supervised learning models (ANN, SVM and NNM) for modeling of mean monthly flow is given in the paper. The 40-years input dataset has been shown as long enough for building models of satisfying quality, and was used in the further analysis where GA and SA were used first for optimization of model parameters, and later, for simultaneous optimization of both model parameters and input variables for SL models. In the analysis, time series was always split in building, calibration and verification part, while optimization was done on the building and calibration part. Data outside the particular time series was used for additional verification. Optimization of the model parameters by the exhaustive search indicated that the most accurate models were ANN and SVM, overperforming NNM across all data subsets, revealing that models need to be built with external variables. Optimization using GA and SA with SVM produced obvious movement toward optimal values, especially when the choice of parameters and input variables was optimized with GA-SVM. [ABSTRACT FROM AUTHOR]

Spectral efficiency (SE) optimization in massive multiple input multiple output (MIMO) antenna cognitive systems is a challenge originated from the coexistence restrictions. Traditional power allocation can optimize the SE; however, involving deep learning can meet real-time and fairness processing requirements. In unfair allocation problem, all power is possibly assigned to one or few antennas of a particular user. In this paper, we build a mathematical optimization model considering the fairness problem such that SE is optimized for all users. To implement the model, we propose an attention-based convolutional neural network (Att-CNN), where h 0 and h k (i.e., cross-interference and direct channels) attention mechanisms are used to improve the SE. The convolutional neural network is applied to decrease the floating point operations (FLOPs) and number of network parameters. We conducted experiments from these aspects: Fair antenna power allocation, power allocation performance and computational performance. Heat maps with different interference thresholds show the fair allocation for all users. Analyses of SE validate the superiority of the Att-CNN compared with the equal power allocation and fully connected neural network (FNN) schemes. The analyses of the FLOPs and number of parameters show the superiority of the Att-CNN over the FNN. [ABSTRACT FROM AUTHOR]

Integrating distributed generations (DGs) into the radial distribution system (RDS) are becoming more crucial to capture the benefits of these DGs. However, the non-optimal integration of renewable DGs and shunt capacitors may lead to several operational challenges in distribution systems, including high energy losses, poor voltage quality, reverse power flow, and lower voltage stability. Therefore, in this paper, the multi-objective optimization problem is expressed with precisely selected three conflicting goals, incorporating the reduction in both power loss and voltage deviation and improvement of voltage stability. A new index for voltage deviation called root mean square voltage is suggested. The proposed multi-objective problems are addressed using two freshly metaheuristic techniques for optimal sitting and sizing multiple SCs and renewable DGs with unity and optimally power factors into RDS, presuming several voltage-dependent load models. These optimization techniques are the multi-objective thermal exchange optimization (MOTEO) and the multi-objective Lichtenberg algorithm (MOLA), which are regarded as being physics-inspired techniques. The MOLA is inspired by the physical phenomena of lightning storms and Lichtenberg figures (LF), while the MOTEO is developed based on the concept of Newtonian cooling law. The MOLA as a hybrid algorithm differs from many in the literature since it combines the population and trajectory-based search approaches. Further, the developed methodology is implemented on the IEEE 69-bus distribution network during several optimization scenarios, such as bi- and tri-objective problems. The fetched simulation outcomes confirmed the superiority of the MOTEO algorithm in achieving accurate non-dominated solutions with fewer outliers and standard deviation among all studied metrics. [ABSTRACT FROM AUTHOR]

This paper addresses the reconstruction issue of sparse signal by developing centralized and distributed neurodynamic approaches. An l 1 -minimization problem can be employed for reconstructing sparse signal, and for solving this problem, a centralized neurodynamic approach with derivative feedback is designed. Considering the fact that the distributed approaches decompose the large-scale problem into small-scale one without central processing node in centralized ones, it effectively reduces the computational complexity of a single node. According to the distributed consensus and graph theory, the original l 1 -minimization problem can be equivalently transformed as a distributed optimization problem. Then, based on our proposed centralized approach, a distributed neurodynamic approach with derivative feedback is further proposed. Through the convex optimization theory and Lyapunov method, we indicate that the optimal solution of l 1 -minimization problem is equivalent to the equilibrium point of centralized or distributed approach, and that each neurodynamic approach globally converges to its equilibrium point. Furthermore, by comparing with several state-of-the-art neurodynamic approaches, our proposed approaches demonstrate their effectiveness and superiority by simulation results in reconstructing sparse signals and images. [ABSTRACT FROM AUTHOR]

Significant improvements of multi-view subspace clustering have emerged in recent years. However, multi-view data are often lying on high-dimensional space and inevitably corrupted by noise and even outliers, which pose challenges for fully exploiting the intrinsic underlying relevance of multi-view data, as the redundant and corrupted features are highly deceptive. To address the above problems, this paper proposes a robust multi-view low-rank embedding (RMLE) method for clustering. Specifically, RMLE projects each high-dimensional view onto a clean low-rank embedding space without energy loss, such that multiple high-quality candidate affinity graphs are yielded by using self-expressiveness subspace learning. Meanwhile, it integrates the clean complimentary information of multi-view data in semantic space to learn a shared consensus affinity graph. Further, an efficient alternating optimization algorithm is designed to solve our RMLE by the alternating direction method of multipliers. Extensive experiments on four benchmark multi-view datasets demonstrate the performance superiority and advantages of RMLE against many state-of-the-art clustering methods. [ABSTRACT FROM AUTHOR]

This paper presents an efficient skull stripping method to improve the decision-making process. Extended Weiner filtering (EWF) is used for removing the noise and enhancing the quality of images. Further, laplacian lion optimization algorithm (LXLOA) is implemented. LXLOA utilizes the Otsu's and Tsallis entropy fitness function to determine an optimal solution. The implemented LXLOA provides a threshold value required for performing the segmentation on the brain MRI images. The extracted features are selected using fuzzy weighted k-means embedding LDA (linear discriminant analysis) method for improving training of the classification model. The proposed LXLOA is extensively tested on standard benchmark functions CEC 2017 and outperforms the existing state-of-the-art algorithm. Rigorous statistical analysis is conducted to determine the statistical significance. Three-fold performance comparison is performed by considering (a) the quality of the segmented image; (b) accuracy, sensitivity, and specificity; and (c) computational cost of convergence for finding an optimal solution. Result reveals that LXLOA gives promising results and demonstrate effective outcomes on the standard quality measures (a) accuracy (97.37%); (b) sensitivity (85.8%); (c) specificity (90%); and (d) precision (91.92%). [ABSTRACT FROM AUTHOR]

Recently, Drones and UAV research were becoming one of the interest topics for academia and industry, where it has been extensively addressed in the literature back the few years. Path planning of drones in an area with complex terrain or unknown environment and restricted by some obstacles is one of the most problems facing the operation of drones. The problem of path planning is not only limited to searching for an appropriate path from the starting point to the destination but also related to how to choose an ideal path among all available paths and provide a mechanism for collision avoidance. By considering how to construct the best path, several related issues need to be taken into account, that relate to safety, obstacle avoidance, response speed to overtake obstacles, etc. Swarm optimization algorithms have been used to provide intelligent modeling for drone path planning and enable to build the best path for each drone. This is done according to the planning and coordination dimensions among the swarm members. In this paper, we have discussed the features and characteristics of different swarm optimization algorithms such as ant colony optimization (ACO), fruit fly optimization algorithm (FOA), artificial bee colony (ABC), and particle swarm optimization (PSO). In addition, the paper provides a comprehensive summary related to the most important studies on drone path planning algorithms. We focused on analyzing the impact of the swarm algorithm and its performance in drone path planning. For that, the paper presented one of the most used algorithms and its models employed to improve the trajectory of drones that rely on swarm intelligence and its impact on the optimal path cost of drones. The results of performance analysis for the ACO algorithm in a 3D and 2D-dimensional environment are illustrated and discussed, and then the performance evaluation of the ACO is compared to the enhanced ACO algorithm. The proposed algorithm achieves fast convergence, accelerating the process of path planning. [ABSTRACT FROM AUTHOR]

Real-world engineering design problems are widespread in various research disciplines in both industry and industry. Many optimization algorithms have been employed to address these kinds of problems. However, the algorithm's performance substantially reduces with the increase in the scale and difficulty of problems. Various versions of the optimization methods have been proposed to address the engineering design problems in the literature efficiently. In this paper, a comprehensive review of the meta-heuristic optimization methods that have been used to solve engineering design problems is proposed. We use six main keywords in collecting the data (meta-heuristic, optimization, algorithm, engineering, design, and problems). It is worth mentioning that there is no survey or comparative analysis paper on this topic available in the literature to the best of our knowledge. The state-of-the-art methods are presented in detail over several categories, including basic, modified, and hybrid methods. Moreover, we present the results of the state-of-the-art methods in this domain to figure out which version of optimization methods performs better in solving the problems studied. Finally, we provide remarkable future research directions for the potential methods. This work covers the main important topics in the engineering and artificial intelligence domain. It presents a large number of published works in the literature related to the meta-heuristic optimization methods in solving various engineering design problems. Future researches can depend on this review to explore the literature on meta-heuristic optimization methods and engineering design problems. [ABSTRACT FROM AUTHOR]

Fuzzy system is one of the most used systems in the decision-making and classification method as it is easy to understand because the way this system works is closer to how humans think. It is a system that uses human experts to hold the membership values to make decisions. However, it is hard to determine the fuzzy parameter manually in a complex problem, and the process of generating the parameter is called fuzzy modelling. Therefore, an optimization method is needed to solve this issue, and one of the best methods to be applied is Butterfly Optimization Algorithm. In this paper, BOA was improvised by combining this algorithm with Harmony Search (HS) in order to achieve optimal results in fuzzy modelling. The advantages of both algorithms are used to balance the exploration and exploitation in the searching process. Two datasets from UCI machine learning were used: Website Phishing Dataset and Phishing Websites Dataset. As a result, the average accuracy for WPD and PWD was 98.69% and 98.80%, respectively. In conclusion, the proposed method shows promising and effective results compared to other methods. [ABSTRACT FROM AUTHOR]

The technological advancement leads to an increase in the usage of the Internet with many applications and connected devices. This increased network size causes increased complexity and creating rooms for the attackers to explore and exploit vulnerabilities to carry out various attacks. As a result upsurge of network attacks can be realized in recent years and is diversified, which can be affirmed by the admittance of various organizations. Varieties of intrusion detection systems (IDSs) have been designed and proposed to tackle such issues based on the misuse-based, anomaly based, and sometimes hybrid techniques. The high rate of network data generation and its enormous volume makes it challenging for IDSs to maintain their efficacy and reliability. This paper discusses a comprehensive understanding of IDS types, six benchmark network datasets, high distributed dimensionality reduction techniques, and classification approaches based on machine learning and deep learning for intrusion detection with their importance to ascertain the efficacy and reliability of IDSs. Furthermore, based on the literature review, a general framework for NIDS has been proposed. At last model for network IDS (NIDS) is designed by following the proposed framework. Achieved accuracy and detection rate of the proposed NIDS model on the UNSW-NB15 dataset are 98.11% and 97.81%, respectively, and achieving better performance than other approaches comparatively. [ABSTRACT FROM AUTHOR]

As the information industry and the Internet develop rapidly, the use of big data enters people's vision and attracts attention. It makes the recommendation system come into being how to quickly extract the desired information from the excessive information. In the recommendation system, user-based collaborative filtering algorithm has become a research hotspot. Existing researches focus on improving collaborative filtering recommendation algorithm by using the kernel method, but still face the cold start problem, the diversity problem, the data sparsity problem, the concept drift problem and more others. To solve these problems, this paper proposes the user-based collaborative filtering based on kernel method and multi-objective optimization (MO-KUCF) which introduces kernel density estimation and multi-objective optimization. It can be increasing diversity of the recommendation systems, improving concept drift in dynamic data and the accuracy and diversity of the recommendation system. The dataset used in this article is the Netflix dataset. It analyzes the MO-KUCF algorithm with the user-based collaborative filtering (UCF) and user-based collaborative filtering based on kernel method (KUCF) by the mean absolute error (MAE). The MAE is compared with the internal user diversity I u index, and the pre-processed data set is divided into the training set and the test set, which are provided to the recommendation system for recommendation and evaluation. The results show that the accuracy of MO-KUCF improves by 5.6%, and the diversity also increases with decreasing values. Combining multi-objective optimization techniques with kernel density estimation methods can improve the diversity of recommendation systems effectively and solve the concept drift problem to achieve the purpose of improving system accuracy. [ABSTRACT FROM AUTHOR]

While improving the operational efficiency of the enterprise, the logistics supply chain directly or indirectly affects the performance of the enterprise because of its own and external uncertainty, resulting in tangible or intangible losses. In this era of rapid change and increasing competition, reducing the impact of uncertainty can reduce the risk and vulnerability of the entire logistics service supply chain, and can gain or maintain a competitive advantage. Therefore, based on the background of supply and demand uncertainty, this paper establishes the fuzzy integrated optimization model of logistics supply chain system by using LR fuzzy numbers. In order to solve this model, the study carried out deterministic processing and transformed it into a deterministic multi-objective linear programming model. At the same time, this study also designed a genetic algorithm to solve the model, in order to solve the choice of potential supply and demand uncertainty in the system, and achieve the global optimization of the network. Finally, the calculations are carried out by numerical examples. The results prove the effectiveness of the model and algorithm. [ABSTRACT FROM AUTHOR]

The popularity of digital cameras and social networks has greatly enriched people's spiritual life, and we can easily obtain massive amounts of digital photos. However, due to the lack of professional guidance and differences in aesthetic appreciation, the photos taken many photographers lack aesthetics. This article is dedicated to the research of image aesthetics, using computers to simulate human perception, and realize the evaluation or beautification of images in line with human aesthetics. In terms of image classification, this article examines the unique perception of human vision on images and proposes new aesthetic features. Combining visual features and semantic features, the SVM algorithm is utilized to build an aesthetic classifier. In the aspect of image optimization, this paper uses the detection of the main image area and the division line of the area and adjusts the main body size and position of the image according to common aesthetic rules, so as to realize the optimization adjustment of the composition of the social image. The experimental results show that the accuracy of social image classification is 97.7%, and the optimized and adjusted images are more aesthetic. [ABSTRACT FROM AUTHOR]

Due to technical advancements and the proliferation of mobile applications, facial analysis (FA) of humans has recently become an important area for computer vision research. FA investigates a variety of difficulties, including gender recognition, facial expression recognition, age and race recognition, with the goal of automatically comprehending social interactions. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This article presents a gender recognition system based on scAOA, that is a modified version of the Archimedes optimization algorithm (AOA). The latest variant (scAOA) enhances the exploitation stage by using trigonometric operators inspired by the sine cosine algorithm (SCA) in order to prevent local optima and to accelerate the convergence. The main purpose of this paper is to apply scAOA to select the relevant deep features provided by two pretrained models of CNN (AlexNet & ResNet) to recognize the gender of a human person categorized into two classes (men and women). Two datasets are used to evaluate the proposed approach (scAOA): the Brazilian FEI dataset and the Georgia Tech Face dataset (GT). In terms of accuracy, Fscore and statistical test, the comparison analysis demonstrates that scAOA outperforms other modern and competitive optimizers such as AOA, SCA, Ant lion optimizer (ALO), Salp swarm algorithm (SSA), Grey wolf optimizer (GWO), Simple genetic algorithm (SGA), Grasshopper optimization algorithm (GOA) and Particle swarm optimizer (PSO). [ABSTRACT FROM AUTHOR]

This paper presents a hybrid model combining the extreme gradient boosting machine (XGBoost) and the whale optimization algorithm (WOA) to predict the bearing capacity of concrete piles. The XGBoost provides the ultimate prediction from a set of explanatory experiment variables. The WOA, which is configured to search for an optimal set of XGBoost parameters, helps increase the model's accuracy and robustness. The hybrid method is constructed by a dataset of 472 samples collected from static load tests in Vietnam. The results indicate that the hybrid model consistently outperforms the default XGBoost model and deep neural network (DNN) regression. In an experiment of 20 runs, the proposed model has gained roughly 12, 11.7, 9, and 12% reductions in root mean square error compared to the DNN with 2, 3, 4, and 5 hidden layers, respectively. The Wilcoxon signed-rank tests confirm that the proposed model is highly suitable for concrete pile capacity prediction. [ABSTRACT FROM AUTHOR]

In this paper, a flexible large-scale ensemble-based optimization algorithm is presented for complex optimization problems. According to the no free lunch theorem, no single optimization algorithm demonstrates superior performance across all optimization problems. Therefore, with the animorphic ensemble optimization (AEO) algorithm presented here, a set of algorithms can be used as an ensemble which demonstrate stronger performance across a wider range of optimization problems than any standalone algorithm. AEO is a high-level ensemble designed to handle large ensembles using a well-defined stochastic migration process. The high-level nature of AEO allows for an arbitrary number of diverse standalone algorithms to interface with one another through an island model interface strategy, where various populations change size according to the performance of the algorithm associated with each population. In this study, AEO is demonstrated using ensembles of both evolutionary and swarm algorithms such as differential evolution, particle swarm, gray wolf optimization, moth-flame optimization, and more, and strong performance is observed. Quantitative diagnostics metrics to describe the migration of individuals across populations are also presented and observed with application to some test problems. In the end, AEO demonstrated strong consistent performance across more than 150 benchmark functions of 10–50 dimensions. [ABSTRACT FROM AUTHOR]

This paper proposes an improved version of the artificial ecosystem-based optimization (AEO) algorithm called artificial ecosystem-based optimization–opposition-based learning (AEO-OBL), with the aim of improving the performance of the original AEO. In addition, it is utilized for determining the optimal allocation of distributed generation (DG) units in radial distribution networks (RDNs) with the aim of minimizing power and energy losses. The stochastic nature of renewable DGs such as wind turbine and photovoltaic generation is taken in consideration using appropriate probability models. The Loss Sensitivity Index is used to assess the most suitable busses for the integration of DG units in the RDN. AEO is nature-inspired optimization algorithm which imitates the flow of energy in an ecosystem on earth. In the proposed AEO-OBL, the search ability and the balance between the exploration and exploitation phases in the original AEO are enhanced. In the AEO-OBL, five efficient strategies are used to avoid falling on a local optimal: (1) enhanced linear weight coefficient a, (2) production operator, (3) modified consumption operator, (4) modified decomposing operator and (5) opposition-based learning (OBL). The performance of the proposed technique is validated on IEEE 33-bus and 85-bus RDNs. To emphasize the superiority of the proposed technique, the results are compared with the original AEO, Henry gas solubility optimizer (HGSO) and Harris hawks optimization (HHO) algorithm results. Besides, the developed algorithm is compared with other optimization algorithms in literature that solved the same problem. The outcomes indicate a better performance of AEO-OBL relative to other algorithms. Accordingly, AEO-OBL can be a very suitable algorithm in solving the problem of optimal DG allocation in RDNs. [ABSTRACT FROM AUTHOR]

Most text clustering algorithms adopt a single criterion optimization approach, which often fails to find good clustering solutions for a wide diversity of datasets with different clustering characteristics. The multi-objective meta-heuristic approach is utilized to seek optimal clustering by maximizing (or minimizing) more than two objective functions. In this paper, we propose a multi-objective memetic differential evolution algorithm (MOMDE) for text clustering. The MOMDE text clustering algorithm combines memetic and differential evolution algorithms to improve the search for optimal clustering by improving the balance between exploitation and exploration. Moreover, a combination with the dominance-based multi-objective approach is employed, which may improve the search for optimal clustering by maximizing or/and minimizing two cluster quality measures. The proposed algorithm is tested on six text clustering datasets from the Laboratory of Computational Intelligence. Our experimental results revealed that the performance of the MOMDE algorithm is better than state-of-the-art text clustering algorithms. Further validation is provided using the F-measure to assess the efficiency of the obtained clustering of MOMDE, whilst the multi-objective performance assessment matrices are used to evaluate the quality of Pareto-optimality. [ABSTRACT FROM AUTHOR]

Evolutionary multi-objective optimization (EMO) refers to the domain in which an evolutionary algorithm is applied to tackle an optimization problem with multiple objective functions. The literature is rich with many approaches proposed to solve multi-objective problems including the NSGA-II, MOEA/D, and MOPSO algorithms. The proposed approaches include stand-alone as well as hybrid techniques. One critical aspect of any evolutionary algorithm (EA) is the stopping criterion. The selection of a specific stopping criterion can have a considerable effect on the performance and the final solution provided by the EA. A number of different stopping criteria, specifically designed for EMO, have been proposed in the literature. In this paper, the performance of six different EMO algorithms is tested and compared using four stopping criteria. The experiments are performed using the ZDT, DTLZ, CEC2009, Tanaka and Srivana test functions. Experimental results are analyzed to highlight the proper stopping criteria for different algorithms. [ABSTRACT FROM AUTHOR]

Image segmentation is a critical step in digital image processing applications. One of the most preferred methods for image segmentation is multilevel thresholding, in which a set of threshold values is determined to divide an image into different classes. However, the computational complexity increases when the required thresholds are high. Therefore, this paper introduces a modified Coronavirus Optimization algorithm for image segmentation. In the proposed algorithm, the chaotic map concept is added to the initialization step of the naive algorithm to increase the diversity of solutions. A hybrid of the two commonly used methods, Otsu's and Kapur's entropy, is applied to form a new fitness function to determine the optimum threshold values. The proposed algorithm is evaluated using two different datasets, including six benchmarks and six satellite images. Various evaluation metrics are used to measure the quality of the segmented images using the proposed algorithm, such as mean square error, peak signal-to-noise ratio, Structural Similarity Index, Feature Similarity Index, and Normalized Correlation Coefficient. Additionally, the best fitness values are calculated to demonstrate the proposed method's ability to find the optimum solution. The obtained results are compared to eleven powerful and recent metaheuristics and prove the superiority of the proposed algorithm in the image segmentation problem. [ABSTRACT FROM AUTHOR]

Nonnegative tensor decomposition is a versatile tool for multiway data analysis, by which the extracted components are nonnegative and usually sparse. Nevertheless, the sparsity is only a side effect and cannot be explicitly controlled without additional regularization. In this paper, we investigated the nonnegative CANDECOMP/PARAFAC (NCP) decomposition with the sparse regularization item using l 1 -norm (sparse NCP). When high sparsity is imposed, the factor matrices will contain more zero components and will not be of full column rank. Thus, the sparse NCP is prone to rank deficiency, and the algorithms of sparse NCP may not converge. In this paper, we proposed a novel model of sparse NCP with the proximal algorithm. The subproblems in the new model are strongly convex in the block coordinate descent (BCD) framework. Therefore, the new sparse NCP provides a full column rank condition and guarantees to converge to a stationary point. In addition, we proposed an inexact BCD scheme for sparse NCP, where each subproblem is updated multiple times to speed up the computation. In order to prove the effectiveness and efficiency of the sparse NCP with the proximal algorithm, we employed two optimization algorithms to solve the model, including inexact alternating nonnegative quadratic programming and inexact hierarchical alternating least squares. We evaluated the proposed sparse NCP methods by experiments on synthetic, real-world, small-scale, and large-scale tensor data. The experimental results demonstrate that our proposed algorithms can efficiently impose sparsity on factor matrices, extract meaningful sparse components, and outperform state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In this paper, shunt capacitors are effectively placed in two radial distribution networks with 69 and 85 nodes for the purpose of reducing the sum of capacitor investment cost and energy loss cost by using a novel metaheuristic, called slime mould optimization algorithm (SMOA). The main duty of SMOA is to find the most suitable position of the shunt capacitors and to determine optimal generation of the shunt capacitors over a year with three load levels. In addition to comparison with previous methods in the literature, SMOA is also compared to two other applied methods including bonobo optimization algorithm (BOA) and tunicate swarm algorithm (TSA). The novelty of the paper is to apply three new methods in which SMOA and TSA were developed in early 2020 and BOA was introduced in 2019. The three methods can reach the same success rate of 100%, but SMOA is more powerful. In fact, SMOA can reach better minimum, mean and maximum total costs, faster convergence speed and more effective stability of fifty independent runs. BOA and TSA cannot find one the same good solution as SMOA even they are run 50 times for each study case. Comparison with previous methods in the literature indicates that SMOA can find better position and more suitable generation for shunt capacitors and it can get less total cost, and use smaller population size and a lower number of iterations. The best result from SMOA is also the main contribution of the study and it is recommended that SMOA should be used for placing capacitors in radial distribution networks. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have evolved as a beneficial machine learning method that has been successfully used in various applications. Currently, DNN is a superior technique of extracting information from massive sets of data in a self-organized method. DNNs have different structures and parameters, which are usually produced for particular applications. Nevertheless, the training procedures of DNNs can be protracted depending on the given application and the size of the training set. Further, determining the most precise and practical structure of a deep learning method in a reasonable time is a possible problem related to this procedure. Meta-heuristics techniques, such as swarm intelligence (SI) and evolutionary computing (EC), represent optimization frames with specific theories and objective functions. These methods are adjustable and have been demonstrated their effectiveness in various applications; hence, they can optimize the DNNs models. This paper presents a comprehensive survey of the recent optimization methods (i.e., SI and EC) employed to enhance DNNs performance on various tasks. This paper also analyzes the importance of optimization methods in generating the optimal hyper-parameters and structures of DNNs in taking into consideration massive-scale data. Finally, several potential directions that still need improvements and open problems in evolutionary DNNs are identified. [ABSTRACT FROM AUTHOR]

This paper presents a novel bio-inspired optimization algorithm called Coronavirus Optimization Algorithm (COVIDOA). COVIDOA is an evolutionary search strategy that mimics the mechanism of coronavirus when hijacking human cells. COVIDOA is inspired by the frameshifting technique used by the coronavirus for replication. The proposed algorithm is tested using 20 standard benchmark optimization functions with different parameter values. Besides, we utilized five IEEE Congress of Evolutionary Computation (CEC) benchmark test functions (CECC06, 2019 Competition) and five CEC 2011 real-world problems to prove the proposed algorithm's efficiency. The proposed algorithm is compared to eight of the most popular and recent metaheuristic algorithms from the state-of-the-art in terms of best cost, average cost (AVG), corresponding standard deviation (STD), and convergence speed. The results demonstrate that COVIDOA is superior to most existing metaheuristics. [ABSTRACT FROM AUTHOR]

Despite that evolutionary and swarm intelligence algorithms have achieved considerable success on multi-objective optimization problems, they face huge challenges when dealing with many-objective optimization problems (MaOPs). There is an urgent call for effective evolutionary and swarm intelligence algorithms for MaOPs. Inspired by the satisfactory performance of bacterial foraging optimization (BFO) on the single-objective optimization problems, this paper extends BFO to deal with MaOPs and proposes a knowledge guided BFO for MaOPs (called as KLBFO). Firstly, KLBFO learns promising direction knowledge based on group decision making idea to guide the population to converge toward proper directions. Secondly, KLBFO learns elite knowledge by a new biological mechanism to accelerate the population to converge. Thirdly, KLBFO learns density knowledge by a new diversity management strategy based on orthogonal grid to produce well-distributed solutions. The performance of KLBFO is comprehensively evaluated by comparing it with eight state-of-the-art algorithms on two suites of test problems and one real-world problem. The empirical results have validated the superior performance of KLBFO for MaOPs. [ABSTRACT FROM AUTHOR]

Nowadays, Internet of Things (IoT) devices are ubiquitous and their number is growing rapidly. These devices produce massive amount of data which need to be efficiently processed. Since most of the IoT devices are resource constrained in terms of computational capability and power resources, they have to offload their computation jobs to more powerful computing devices. Fog–cloud computing is a promising platform for processing IoT jobs. However, due to the heterogeneity of the computing devices, how to schedule IoT jobs in this environment is a challenging issue. To tackle this issue, in this paper, we first present a system model for the job scheduling problem in fog–cloud computing with the aim of optimizing the total deadline violation time of jobs and the energy consumption of the system. Then, we propose two nature-inspired optimization techniques, grey wolf optimization and grasshopper optimization algorithm to efficiently solve the job scheduling problem in the fog–cloud environment. The performance of the proposed algorithms is evaluated against the state-of-the-art algorithms using various simulation experiments. The results demonstrate that the proposed schedulers are capable of reducing the total deadline violation time about 68% and energy consumption about 22% compared to the second-best results. [ABSTRACT FROM AUTHOR]

Data and text clustering are popular and frequently used in the data mining domain, mainly to deal with big data analytics. The main problem in these techniques is finding the most coherent clusters allocating similar-related objects into one group. In this paper, an improved clustering analysis approach is proposed using an advanced optimization method called AOAOA. The proposed AOAOA method improved the Aquila optimizer (AO) search performance by the operators of the arithmetic optimization algorithms (AOA) and differential evolution (DE) and using a novel transition mechanism. The primary motivation for this modification is that the original optimizer suffers from local optima stagnation and lacks search balance. Thus, the proposed AOAOA overcame these shortcomings by integrating various powerful search strategies and a new update strategy. Experiments are conducted on two parts; eight standard data clustering datasets and ten text documents benchmark datasets to evaluate the performance of the proposed AOAOA method. The proposed method is compared against several well-known optimization algorithms and advanced state-of-the-art methods published in the literature. The data clustering results also showed promising performance for the proposed AOAOA compared to other comparative data clustering methods. Moreover, the results illustrated that the proposed AOAOA can find new best solutions for several different complicated cases as the text document clustering results. The proposed AOAOA got accurate and robust results compared to several state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Multi-objective evolutionary algorithms face many challenges in optimizing mixed-variable multi-objective problems, such as quantization error, low search efficiency of discontinuous discrete variables, and difficulty in coding non-integer discrete variables. To overcome these challenges, this paper proposes a mixed-variable multi-objective evolutionary algorithm based on estimation of distribution algorithm (MVMO-EDA). Compared with traditional multi-objective evolutionary algorithms, MVMO-EDA has the following improvements: (1) instead of crossover and mutation, statistics and sampling are used to generate offspring, which can avoid the quantization error caused by crossover and mutation operations; (2) using index coding for discrete variables to improve the search efficiency; and (3) a scalable histogram probability distribution model and two crowding distance-based diversity maintenance strategies are used to improve the global optimization ability. The performance of the proposed MVMO-EDA is evaluated on the modified ZDT and DTLZ benchmark sets with mixed-variable, and the results show that MVMO-EDA has a competitive performance both in convergence and diversity. [ABSTRACT FROM AUTHOR]

Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies. [ABSTRACT FROM AUTHOR]

Nature-inspired optimization techniques have been applied in various fields of study to solve optimization problems. Since designing a Fuzzy System (FS) can be considered one of the most complex optimization problems, many meta-heuristic optimizations have been developed to design FS structures. This paper aims to design a Takagi–Sugeno–Kang fuzzy Systems (TSK-FS) structure by generating the required fuzzy rules and selecting the most influential parameters for these rules. In this context, a new hybrid nature-inspired algorithm is proposed, namely Genetic–Grey Wolf Optimization (GGWO) algorithm, to optimize TSK-FSs. In GGWO, a hybridization of the genetic algorithm (GA) and the grey wolf optimizer (GWO) is applied to overcome the premature convergence and poor solution exploitation of the standard GWO. Using genetic crossover and mutation operators accelerates the exploration process and efficiently reaches the best solution (rule generation) within a reasonable time. The proposed GGWO is tested on several benchmark functions compared with other nature-inspired optimization algorithms. The result of simulations applied to the fuzzy control of nonlinear plants shows the superiority of GGWO in designing TSK-FSs with high accuracy compared with different optimization algorithms in terms of Root Mean Squared Error (RMSE) and computational time. [ABSTRACT FROM AUTHOR]

Slime mould algorithm (SMA) is a novel metaheuristic algorithm with good performance for optimization problems, but it may encounter premature or low accuracy in complex optimization problems. This paper presents a hybrid SMA using teaching–learning based optimization (TLBO), called TLSMA, for solving global optimization and reliability-based design optimization (RBDO) problems. The key point of TLSMA is to combine the capacities of exploration and exploitation from SMA and TLBO, which can enhance the convergence ability of SMA. Moreover, the TLSMA is extended for solving RBDO problems under probabilistic constraints, which are handled by the adaptive chaos control method. The proposed algorithm is tested by a series of experiments with two parts. First, TLSMA is verified by 24 well-known benchmark optimization problems with unimodal and multimodal functions, and is compared with several state-of-the-art metaheuristic algorithms. The results of benchmark optimization problems show that TLSMA outperforms PSO, BBO, GWO, WOA, SSA, TLBO and SMA. Then, TLSMA-RBDO is tested by five RBDO problems, including a numerical and four engineering problems. The results illustrate that the proposed algorithm has high performance in the RBDO problems, which is significantly superior to the compared algorithms. [ABSTRACT FROM AUTHOR]

Time series modelling is gaining spectacular popularity in the prediction process of decision making, with applications including real-world management and engineering. However, for short time series, prediction has to face unavoidable limitation for modelling extremely complex systems. It has to apply inadequate and incomplete data from short time to predict unknown observations. With such limited data source, existing techniques, such as statistical modelling or machine learning methods, fail to predict short time series effectively. To address this problem, this paper provides a global framework for short time series modelling predictions, integrating the rolling mechanism, grey model, and meta-heuristic optimization algorithms. In addition, dragonfly algorithm and whale optimization algorithm are investigated and deployed to optimize the framework by enhancing its predicting accuracy with less computational costs. To verify the performance of the proposed framework, three industrial cases are introduced as simulation experiments in this paper. Experimental results confirm that the framework solves corresponding short time series modelling predictions with greater accuracy and speed than the standard GM(1,1) models. The source codes of this framework are available at: https://github.com/zhesencui/HybridRollingGreyFramework.git. [ABSTRACT FROM AUTHOR]

Due to the characteristics of time-varying traffic and nonlinearity, the short-term traffic flow data are difficult to predict accurately. The purpose of this paper is to improve the short-term traffic flow prediction accuracy through the proposed improved wavelet neural network prediction model and provide basic data and decision support for the intelligent traffic management system. In view of the extremely strong nonlinear processing power, self-organization, self-adaptation and learning ability of wavelet neural network (WNN), this paper uses it as the basic prediction model and uses the particle swarm optimization algorithm for the slow convergence rate and local optimal problem of WNN prediction algorithm. With the advantages of fast convergence, high robustness and strong global search ability, an improved particle swarm optimization algorithm is proposed to optimize the wavelet neural network prediction model. The improved wavelet neural network is used to predict short-term traffic flow. The experimental results show that the proposed algorithm is more efficient than the WNN and PSO–WNN algorithms alone. The prediction results are more stable and more accurate. Compared with the traditional wavelet neural network, the error is reduced by 14.994%. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have been proposed to solve several optimization problems in different research areas due to their unique attractive features. Traditionally, heuristic approaches are designed separately for discrete and continuous problems. This paper leverages the meta-heuristic algorithm for solving NP-hard problems in both continuous and discrete optimization fields, such as nonlinear and multi-level programming problems through extensive simulations of volcano eruption process. In particular, a new optimization solution named volcano eruption algorithm is proposed in this paper, which is inspired from the nature of volcano eruption. The feasibility and efficiency of the algorithm are evaluated using numerical results obtained through several test problems reported in the state-of-the-art literature. Based on the solutions and number of required iterations, we observed that the proposed meta-heuristic algorithm performs remarkably well to solve NP-hard problem. Furthermore, the proposed algorithm is applied to solve some large-size benchmarking LP and Internet of vehicles problems efficiently. [ABSTRACT FROM AUTHOR]

In this paper, to keep the researchers interested in nature-inspired algorithms and optimization problems, a comprehensive survey of the group search optimizer (GSO) algorithm is introduced with detailed discussions. GSO is a nature-inspired optimization algorithm introduced by He et al. (IEEE Trans Evol Comput 13:973–990, 2009) to solve several different optimization problems. It is inspired by animal searching behavior in real life. This survey focuses on the applications of the GSO algorithm and its variants and results from the year of its suggestion (2009) to now (2020). GSO algorithm is used to discover the best solution over a set of candidate solution to solve any optimization problem by determining the minimum or maximum objective function for a specific problem. Meta-heuristic optimizations, nature-inspired algorithms, have become an interesting area because of their rule in solving various decision-making problems. The general procedures of the GSO algorithm are explained alongside with the algorithm variants such as basic versions, discrete versions, and modified versions. Moreover, the applications of the GSO algorithm are given in detail such as benchmark function, classification, networking, engineering, and other problems. Finally, according to the analyzed papers published in the literature by the all publishers such as IEEE, Elsevier, and Springer, the GSO algorithm is mostly used in solving various optimization problems. In addition, it got comparative and promising results compared to other similar published optimization algorithm. [ABSTRACT FROM AUTHOR]

Convex optimization techniques are extensively applied to various models, algorithms, and applications of machine learning and data mining. For optimization-based classification methods, the sparsity principle can greatly help to select simple classifier models, while the single- and multi-kernel methods can effectively address nonlinearly separable problems. However, the limited sparsity and kernel methods hinder the improvement of the predictive accuracy, efficiency, iterative update, and interpretable classification model. In this paper, we propose a new Explainable Multi-sparsity Multi-kernel Nonconvex Optimization Least-squares Classifier (EM2NOLC) model, which is an optimization problem with a least-squares objective function and multi-sparsity multi-kernel nonconvex constraints, aiming to address the aforementioned issues. Based on reconstructed multiple kernel learning (MKL), the proposed model can extract important instances and features by finding the sparse coefficient and kernel weight vectors, which are used to compute importance or contribution to classification and obtain the explainable prediction. The corresponding EM2NOLC algorithm is implemented with the Alternating Direction Method of Multipliers (ADMM) method. On the real classification datasets, compared with the three ADMM classifiers of Linear Support Vector Machine Classifier, SVMC, Least Absolute Shrinkage and Selection Operator Classifier, the two MKL classifiers of SimpleMKL and EasyMKL, and the gradient descent classifier of Feature Selection for SVMC, our proposed EM2NOLC generally obtains the best predictive performance and explainable results with the least number of important instances and features having different contribution percentages. [ABSTRACT FROM AUTHOR]

The third type of neural network called spiking is developed due to a more accurate representation of neuronal activity in living organisms. Spiking neural networks have many different parameters that can be difficult to adjust manually to the current classification problem. The analysis and selection of coefficients' values in the network can be analyzed as an optimization problem. A practical method for automatic selection of them can decrease the time needed to develop such a model. In this paper, we propose the use of a heuristic approach to analyze and select coefficients with the idea of collaborative working. The proposed idea is based on parallel analyzing of different coefficients and choosing the best of them or average ones. This type of optimization problem allows the selection of all variables, which can significantly affect the convergence of the accuracy. Our proposal was tested using network simulators and popular databases to indicate the possibilities of the described approach. Five different heuristic algorithms were tested and the best results were reached by Cuckoo Search Algorithm, Grasshopper Optimization Algorithm, and Polar Bears Algorithm. [ABSTRACT FROM AUTHOR]

Harris hawks optimization (HHO) algorithm, which is inspired from Harris hawks hunting strategy, uses uniform random numbers in the optimization process. This paper proposes modifying HHO with seven types of random distribution function definitions that are chi-square distribution, normal distribution, exponential distribution, Rayleigh distribution, Student's distribution, F distribution, and lognormal distribution to show effects on stochastic search-based optimization algorithm performance. The modified HHO algorithm is tested via some benchmark test functions. Results are compared with each other and with classical HHO solutions. Then, the HHO and its modified versions are applied to optimum power flow (OPF), which is an important problem for power system engineering for decades. The algorithms are applied to IEEE 30-bus test system to minimize total fuel cost of the power system, active/reactive power losses, and emission, by comparing with recent OPF researches. Considering the applicability of the proposed approach and the results achieved, one can confirm that it might be a different alternative method for solving OPF problems. One of the important results of the paper in the IEEE 30-bus test system is that the cost of fuel is calculated as 798.9105 $/h with classical HHO, while it is calculated as 798.66 $/h with the HHO modified with SD function. [ABSTRACT FROM AUTHOR]

Assembly sequence planning is one of the key issues in DFA and computer-aided assembly process planning research for concurrent engineering. The purpose of this paper is to solve the problem of insufficient individual intelligence in evolutionary algorithms for assembly sequence planning, and a evolutionary algorithm for assembly sequence planning is designed. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the hybrid assembly sequence planning and assembly line balance problems. According to the assembly sequence problem, the number of assembly tool changes and the number of assembly orientation changes are transformed into the operation time of the assembly line. At the same time, the transportation of heavy parts in the assembly balance problem is considered. Then, by extracting the connection relationship and information of the parts, the disassembly method is used to inversely obtain the disassembly support matrix, and then, it is used to obtain the priority relationship diagram of the assembly operation tasks that indicate the order constraints of the job tasks on the assembly line. Aiming at the shortcoming that particle swarm optimization algorithm is easy to fall into local optimum, a various population strategy is adopted to shorten the evolution stagnation time, improve the evolution efficiency of particle swarm optimization algorithm, and enhance the optimization ability of the algorithm. Combined with the three evaluation indicators of assembly geometric feasibility, assembly process continuity, and assembly tool change times, a fitness function is constructed to achieve multi-objective optimization. Finally, experiments show that the multi-agent evolutionary algorithm is incorporated into the planning process to obtain an accurate solution through the various population strategy–particle swarm optimization algorithm, which proves the feasibility of the compound algorithm and has better performance in solving assembly sequence planning problems. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is a recently developed meta-heuristic swarm intelligence optimization algorithm based on simulating the chain movement behavior of salps sailing and foraging in the sea. In this paper, a novel hybrid meta-heuristic algorithm called SSA-FGWO is proposed to overcome the shortcomings of the original SSA, including slow convergence speed in dealing with high-dimensional and multimodal landscapes, low precision, and global optimization problems. The essential idea of SSA-FGWO is to improve the salp swarm optimization algorithm (SSA) by utilizing the Grey Wolf Algorithm (GWO) strategy. The hybridization mechanism includes two steps: First, the strong exploitation of SSA is applied to update the leaders' position of the chain population. Second, the strong exploration strategy of GWO is used to update the followers' position to increase the population variance of the proposed optimizer. The proposed algorithm is expected to enhance exploration and exploitation ability significantly by the hybrid design. The effectiveness of SSA-FGWO is investigated through CEC2020, 23 representative benchmark cases, and feature selection problems (18 data sets) are solved. The algorithm has been examined for its computational complexity and convergent behavior. In addition to GWO, SSA and other swarm optimization algorithms are employed to compare with the proposed optimizer. The obtained experimental results show that the exploitation, exploration tendencies, and convergence patterns of SSA-FGWO have significantly improved. The results show that the proposed SSA-FGWO algorithm is a promising one that outperforms the basic SSA, GWO, and other algorithms in terms of efficacy. [ABSTRACT FROM AUTHOR]