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In soil slope stability, locating the critical slip surface with the minimum safety factor is a difficult and complicated optimization problem. Most methods fail when proper bounds are not applied to the decision variable. For the first time, the paper uses the prey–predator algorithm to analyze slopes stability with circular slip surfaces making use of meaningful rules, from an engineering point of view, to define these bounds. The Fellenius method based on limit equilibrium technique also serves as the constraint of the mathematical problem adopted to solve the task. A pseudo-analytical study is also carried out on four benchmark literature problems to test the performance and to certify the accuracy of the results obtained with the new application of the prey–predator algorithm. We show that the solutions obtained are accurate and should be assumed as a reference comparing to other methods. • The prey–predator metaheuristic is used for soil slope stability analysis. • A new approach to bound the decision variables which is meaningful from an engineering point of view is proposed. • A pseudo analytical study is proposed to certify the quality of the results. • New best results are presented and validated. [ABSTRACT FROM AUTHOR] more...

Portable Document Format (PDF) is a file format created to create portable and printable documents across platforms. PDF files are one of the most widely used application types in computer-based systems. Thanks to the functionality that PDF files provide, they are used by many users around the world. Malware developers can exploit PDF files due to various factors. Malware can integrate embedded files, JavaScript, PDF files, etc. As a result, PDFs are susceptible to security vulnerabilities in computer-based systems. In this study, we utilised the CIC-Evasive-PDFMal2022 dataset, made accessible by the Canadian Cybersecurity Institute in 2022, that includes two categories, namely benign and malicious. In the preprocessing step, the proposed model transformed text-based PDF parameter data into the 2D PDF417 barcode. 2D Convolutional Neural Network (CNN) models (MobileNetV2, ResNet18, and ShuffleNet) are trained using the dataset generated by the preprocessing step. CNN is a type of artificial neural network used in image recognition, processing, and classification. Type/class based feature sets were then obtained by each CNN model. In the last step, the metaheuristic optimization method (Honey Badger Algorithm) was used. Thanks to this method, the best performing feature set was determined among the feature sets of the types extracted from each CNN model. It was then classified by the softmax method, and an overall accuracy of 99.73% was achieved. The proposed approach has successfully trained 1D data with 2D CNNs. In addition, with the barcode imaging technique, direct understanding of the data by the users is prevented. [Display omitted] • Thanks to the proposed approach, the classification of 1D PDF metadata with 2D CNN models has been successfully accomplished. • 1D data was converted to the PDF417 barcode type, resulting in 2D images. • Training of 2D images with 2-D CNN models paved the way and successful trainings took place. • Type-based feature sets were created by adding a new fully connected layer to the last layer of the CNN model. • Thanks to the HBA method, the best feature sets were selected among the types and classified with 99.73% success. [ABSTRACT FROM AUTHOR] more...

A central problem in network dynamics is understanding how influence spreads through a social network. This problem can be studied from an optimization approach. The aim is to find an initial seed of actors, with certain size restrictions, capable of maximizing or minimizing the activation of other actors in the network through a given influence spread model. The maximization and minimization versions of this problem have been extensively studied. In recent years, the min–max multi-objective version was defined, which involves finding the smallest seed capable of maximizing the influence spread in the network. Searching for exact solutions in these optimization problems is not feasible, even for relatively small networks. Hence, various approximation techniques have been proposed in recent years, with bio-inspired algorithms based on metaheuristics standing out among them. However, the max–min multi-objective version of the problem remains open. This article formally defines the max–min influence spread problem, aiming to find the maximum seed with the minimum spread capacity. We propose a strategy that uses solutions from the min–max version of the problem to reduce the search space, allowing us to avoid trivial solutions. The potential applications of this max–min version are diverse, e.g., finding clusters less susceptible to diseases in a contagion network or the most inefficient coalitions in a voting system. Using swarm intelligence metaheuristics methods as in the min–max version, the results obtained on real social networks show that this approach exhibits rapid convergence, reaching a seed encompassing 51.3% of the actors who could not influence others within the network. Similarly, for a more complex network, the approach is able to generate a seed where 71.8% of the actors showed no influence over others. • We define the max–min influence spread problem as multi-objective optimization. • We solve it using swarm intelligence-based methods on real social networks. • The PSO algorithm allows efficient solution sets with non trivial solutions. [ABSTRACT FROM AUTHOR] more...

In this article, a series of learning strategies are proposed to enhance the optimization ability of the artificial electric field algorithm. Orthogonal learning is an important mathematical tool that can greatly influence the adaptability of population-based optimization algorithms. This article proposes, (i) an orthogonal array-based learning strategy to generate a better initial population for the artificial electric field algorithm. Along with the changes in the initialization mechanism, this article also proposes, (ii) an archive-based self-adaptive learning strategy for an artificial electric field algorithm. The proposed learning strategy divides the population into ordinary and extraordinary sub-populations, each with distinct learning mechanisms. The ordinary sub-population utilizes six learning strategies based on three archives, which contain individuals of different quality levels. We incorporate, (iii) a mutation strategy also to update the extraordinary sub-population. Finally, (iv) a self-adaptive strategy is implemented to dynamically adjust the parameters of the proposed algorithm. The effectiveness of these mechanisms is assessed through an extensive analysis of exploration–exploitation dynamics and diversity. Furthermore, an independent structural study is conducted to examine the impact of implemented mechanisms on the algorithm's behavior and efficiency. The proposed algorithm is evaluated on real parameter CEC 2017 problems across different dimensional search spaces. It is compared to eleven state-of-the-art algorithms, and the results demonstrate superior performance in terms of solution accuracy, convergence rate, search capability, and stability. The overall ranking highlights its exceptional potential for solving challenging optimization problems. Additionally, it outperforms other state-of-the-art algorithms across various dimensions, achieving accuracy rates of 64.48%, 70.05%, 78.73%, and 79.25% for dimensions 10, 30, 50, and 100, respectively. Furthermore, it demonstrates superior performance, outperforming others in 73.13% and 60.61% of the problems concerning average accuracy and statistical significance across all dimensions, respectively. [Display omitted] • Introducing an archive-based self-adaptive artificial electric field algorithm (AEFA) with orthogonal initialization for real-parameter optimization problems. • Proposal of an orthogonal array into the AEFA to initialize the population along with a self-adaptive parameter mechanism. • A novel strategy is proposed to divide the population into two homogeneous sub-populations. • Introduction of six learning strategies based on three elite archives and utilizing mutation mechanism. • A rigorous analysis of the proposed algorithm in terms of diversity, exploration–exploitation, convergence, and statistical validation. [ABSTRACT FROM AUTHOR] more...

Although the high number of bands in hyperspectral remote sensing images increases their usefulness, it also causes some processing difficulty. In supervised classification, one problem is decreasing classification accuracy due to the insufficient training samples against the bands. A way to deal with this problem is the selection of appropriate bands by the metaheuristic methods. Because of the stochastic search, the selected bands differ in any implementation of a metaheuristic method. So, the results obtained from the classification of these different band subsets will also have some differences. In this study, a fusion-based approach has been proposed to improve the classification of hyperspectral remote sensing images by multiple implementations of a metaheuristic method for band selection. We have tested the proposed method using ten metaheuristic methods with different objective functions on four well-known datasets. The results show the proposed fusion-based approach successfully improves the classification accuracy in all experiments. The accuracy improvement varies depending on the metaheuristic method, the objective function, and the dataset and ranges from 0.4% to 15.7%. The proposed method improves the classification of complex datasets more and affects weaker objective functions considerably. The results also show the proposed method brings the accuracy of different metaheuristic methods close to each other and reduces the sensitivity of selecting the proper ones. Thus, an automated classification system can be obtained using a parameter-less method. • A new fusion-based classification is proposed for hyperspectral images based on stochastic nature of metaheuristic methods. • A fully automated classification system is developed to classify hyperspectral remote sensing images. • Many Traditional and new metaheuristic methods include PSO, CSO, GWO, GEO, JSA, ... are examined in the proposed framework. • The experiments are done in both case of filter and wrapper band selection with different objective function. [ABSTRACT FROM AUTHOR] more...

This paper presents the application of optimization metaheuristics in direct torque control with space vector modulation (DTC-SVM) of a three-phase induction motor. Two metaheuristic algorithms are considered: Ant Colony Optimization (ACO) and Differential Evolution (DE). These techniques are considered in order to achieve an optimized tuning of proportional-integral (PI) controllers in the DTC-SVM control loops, such as rotor speed, electromagnetic torque, stator flux linkage and estimation of the linkage stator flux. Hence, the paper aims to contribute to adjusting the PI controllers of DTC-SVM. All the optimization procedure is performed via computer simulation. Once the optimized gains are obtained, they are applied to the practical system developed herein. Simulation and experimental results are presented in order to validate the approach proposed. [ABSTRACT FROM AUTHOR] more...

Cancer is one of the most dangerous diseases globally, causing adverse effects on human life, with early detection and treatment planning being crucial for patients. Amongst different malignancies, Lung and Colorectal cancer cause the first and second most cancer deaths in the world, respectively. In this study, the authors aim to analyze LC25000 histopathological image dataset for lung and colon cancer detection. The fundamental goal of the proposed research is to leverage the ensemble learning approach to improve the classification performance of deep learning models. Many previous studies have proposed several ensemble methods and weighting schemes. However, none of them optimized the assigned weights using a meta-heuristic-based approach as per our best knowledge. The authors have applied Differential Evolution optimization to optimize and find the optimal assigned weights to the classifiers while training the ensemble model. In addition, a novel approach to ensemble base learners with majority voting based on Condorcet's Jury Theorem has also been proposed. This proposed method has been shown to save a lot of computational efforts by eliminating the training procedure of meta-learners. Besides this, the authors also demonstrated that Condorcet's Jury Theorem holds while ensembling the N number of classifiers in Neural Networks. Our proposed method and experimental results outperformed compared to the state-of-the-art with the optimized ensemble model showing an accuracy of 99.78% and Condorcet's Jury Theorem-based ensemble model 99.88% on 5-class classification. • Majority voting based on Condorcet's Jury Theorem. • Deep feature extraction using various pre-trained networks. • Novel method to assign weights to the base learners in an ensemble model. • A meta-heuristic-based optimization — Differential Evolution. • Condorcet's Jury Theorem demonstration in neural networks. [ABSTRACT FROM AUTHOR] more...

Visual sensor networks (VSNs) consist of spatially distributed video cameras that are capable of compressing and transmitting the video sequences they acquire. We consider a direct-sequence code division multiple access (DS-CDMA) VSN, where each node has its individual requirements in compression bit rate and energy consumption, depending on the corresponding application and the characteristics of the monitored scene. We study two optimization criteria for the optimal allocation of the source and channel coding rates, which assume discrete values, as well as for the power levels of all nodes, which are continuous, under transmission bit rate constraints. The first criterion minimizes the average distortion of the video received by all nodes, while the second one minimizes the maximum video distortion among all nodes. The resulting mixed integer optimization problems are tackled with a modern optimization algorithm, namely particle swarm optimization (PSO), as well as a hybrid scheme that combines PSO with the deterministic Active-Set optimization method. Extensive experimentation on interference-limited as well as noisy environments offers significant intuition regarding the effectiveness of the considered optimization schemes, indicating the impact of the video sequence characteristics on the joint determination of the transmission parameters of the VSN. [ABSTRACT FROM AUTHOR] more...

This paper proposes Komodo Mlipir Algorithm (KMA) as a new metaheuristic optimizer. It is inspired by two phenomena: the behavior of Komodo dragons living in the East Nusa Tenggara, Indonesia, and the Javanese gait named mlipir. Adopted the foraging and reproduction of Komodo dragons, the population of a few Komodo individuals (candidate solutions) in KMA are split into three groups based on their qualities: big males, female, and small males. First, the high-quality big males do a novel movement called high-exploitation low-exploration to produce better solutions. Next, the middle-quality female generates a better solution by either mating the highest-quality big male (exploitation) or doing parthenogenesis (exploration). Finally, the low-quality small males diversify candidate solutions using a novel movement called mlipir (a Javanese term defined as a walk on the side of the road to reach a particular destination safely), which is implemented by following the big males in a part of their dimensions. A self-adaptation of the population is also proposed to control the exploitation–exploration balance. An examination using the well-documented twenty-three benchmark functions shows that KMA outperforms the recent metaheuristic algorithms. Besides, it provides high scalability to optimize thousand-dimensional functions. The source code of KMA is publicly available at: https://suyanto.staff.telkomuniversity.ac.id/komodo-mlipir-algorithm and https://www.mathworks.com/matlabcentral/fileexchange/102514-komodo-mlipir-algorithm. • A Komodo Mlipir Algorithm (KMA) is proposed as a novel metaheuristic optimizer. • A new coordination is created by three groups of individuals with different strategies. • A mlipir movement is introduced as a low-exploitation high-exploration strategy. • KMA is stable and scalable for thousand-dimensional classic benchmark functions. [ABSTRACT FROM AUTHOR] more...

Deep learning techniques usually face drawbacks related to the vanishing gradient problem, i.e., the gradient becomes gradually weaker when propagating from one layer to another until it finally vanishes away and no longer helps in the learning process. Works have addressed this problem by introducing residual connections, thus assisting gradient propagation. However, such a subject of study has been poorly considered for Deep Belief Networks. In this paper, we propose a weighted layer-wise information reinforcement approach concerning Deep Belief Networks. Moreover, we also introduce metaheuristic optimization to select proper weight connections that improve the network's learning capabilities. Experiments conducted over public datasets corroborate the effectiveness of the proposed approach in image classification tasks. • Novel DBN with weight-based residual connections between layers. • Reinforcement and regularization of the information flow. • Application of metaheuristic optimization to fine-tune Res-DBN hyperparameters; [ABSTRACT FROM AUTHOR] more...

Image segmentation is an essential part of image analysis, which has a direct impact on the quality of image analysis results. Thresholding is one of the simplest and widely used methods for image segmentation. Thresholding can be either bi-level, which involves partitioning of an image into two segments, or multilevel, which partitions an image into multiple segments using multiple thresholds values. This paper focuses on multilevel thresholding. A good segmentation scheme through multilevel thresholding identifies suitable threshold values to optimize between-class variance or entropy criterion. For such optimizations, nature inspired metaheuristic algorithms are commonly used. This paper presents a Kapur's entropy based Crow Search Algorithm (CSA) to estimate optimal values of multilevel thresholds. Crow Search Algorithm is based on the intelligent behavior of crow flock. Crow Search Algorithm have shown better results because of less number of parameters, no premature convergence, and better exploration–exploitation balance in the search strategy. Kapur's entropy is used as an objective function during the optimization process. The experiments have been performed on benchmarked images for different threshold values (i.e. 2, 4, 8, 16, 32 thresholds). The proposed method has been assessed and performance is compared with well-known metaheuristic optimization methods like Particle Swarm Optimization (PSO), Differential Evolution (DE), Grey Wolf Optimizer (GWO), Moth-Flame Optimization (MFO) and Cuckoo Search (CS). Experimental results have been evaluated qualitatively and quantitatively by using well-performed evaluation methods namely PSNR, SSIM, and FSIM. Computational time and Wilcoxon p-type value also compared. Experimental results show that proposed algorithm performed better than PSO, DE, GWO, MFO and CS in terms of quality and consistency. [ABSTRACT FROM AUTHOR] more...

This study developed a novel optimization algorithm, called Forensic-Based Investigation (FBI), inspired by the suspect investigation–location–pursuit process that is used by police officers. Although numerous unwieldy optimization algorithms hamper their usability by requiring predefined operating parameters, FBI is a user-friendly algorithm that does not require predefined operating parameters. The performance of parameter-free FBI was validated using four experiments: (1) The robustness and efficiency of FBI were compared with those of 12 representations of the top leading metaphors by using 50 renowned multidimensional benchmark problems. The result indicated that FBI remarkably outperformed all other algorithms. (2) FBI was applied to solve a resource-constrained scheduling problem associated with a highway construction project. The experiment demonstrated that FBI yielded the shortest schedule with a success rate of 100%, indicating its stability and robustness. (3) FBI was utilized to solve 30 benchmark functions that were most recently presented at the IEEE Congress on Evolutionary Computation (CEC) competition on bound-constrained problems. Its performance was compared with those of the three winners in CEC to validate its effectiveness. (4) FBI solved high-dimensional problems, by increasing the number of dimensions of benchmark functions to 1000. FBI is efficient because it requires a relatively short computational time for solving problems, it reaches the optimal solution more rapidly than other algorithms, and it efficaciously solves high-dimensional problems. Given that the experiments demonstrated FBI's robustness, efficiency, stability, and user-friendliness, FBI is promising for solving various complex problems. Finally, this study provided the scientific community with a metaheuristic optimization platform for graphically and logically manipulating optimization algorithms. • A novel metaheuristic Forensic-Based Investigation (FBI) algorithm is proposed. • The FBI algorithm does not require to preset the tuning parameters. • The robustness and efficiency of FBI algorithm are compared with those of leading metaphors in solving high-dimensional and real problems. • FBI outperforms all other algorithms with faster convergence and a shorter computational time. • A graphical platform for implementing the new algorithm and others is provided for the ease of use. [ABSTRACT FROM AUTHOR] more...

Simulated Annealing (SA) is a single-solution-based metaheuristic technique based on the annealing process in metallurgy. It is also one of the best-known metaheuristic algorithms due to its simplicity and good performance. Despite its interesting characteristics, SA suffers from several limitations such as premature convergence. On the other hand, Japanese swordsmithing refers to the manual-intensive process for producing high-quality bladed weapons from impure raw metals. During this process, Japanese smiths fold and reheat pieces of metal multiple times in order to eliminate impurities and defects. In this paper, an improved version of the SA algorithm is presented. In the new approach, a population of agents is considered. Each agent conducts a search strategy based on a modification of the SA scheme. The proposed algorithm modifies the original SA incorporating two new operators, folding and reheating, inspired by the ancient Japanese Swordsmithing technique. Under the new approach, the process of folding is conceived as a compression of the search space, while the reheating mechanism considers a reinitialization of the cooling process in the original SA scheme. With this inclusion, the new algorithm maintains the computational structure of the SA method but improving its search capacities. In order to evaluate its performance, the proposed algorithm is tested in a set of 28 benchmark functions, which include multimodal, unimodal, composite and shifted functions, and 3 real world optimization problems. The results demonstrate the high performance of the proposed method when compared to the original SA and other popular state-of-the-art algorithms. • A novel metaheuristic method has been introduced based on the SA algorithm. • Two new operators have been designed to handle exploitation and exploration. • Both operators have been included in the original SA to improve its search abilities. • Experimental results demonstrate the superiority of the proposed approach. [ABSTRACT FROM AUTHOR] more...