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This review aims to exploit a study on different benchmark test functions used to evaluate the performance of Meta-Heuristic (MH) optimization techniques. The performance of the MH optimization techniques is evaluated with the different sets of mathematical benchmark test functions and various real-world engineering design problems. These benchmark test functions can help to identify the strengths and weaknesses of newly proposed MH optimization techniques. This review paper presents 215 mathematical test functions, including mathematical equations, characteristics, search space and global minima of the objective function and 57 real-world engineering design problems, including mathematical equations, constraints, and boundary conditions of the objective functions carried out from the literature. The MATLAB code references for mathematical benchmark test functions and real-world design problems, including the Congress of Evolutionary Computation (CEC) and Genetic and Evolutionary Computation Conference (GECCO) test suite, are presented in this paper. Also, the winners of CEC are highlighted with their reference papers. This paper also comprehensively reviews the literature related to benchmark test functions and real-world engineering design challenges using a bibliometric approach. This bibliometric analysis aims to analyze the number of publications, prolific authors, academic institutions, and country contributions to assess the field's growth and development. This paper will inspire researchers to innovate effective approaches for handling inequality and equality constraints. [ABSTRACT FROM AUTHOR]

Aiming at the shortcomings of Chimp optimization algorithm (ChOA), which is easy to fall into local optimal value and imbalance between global exploration ability and local exploitation ability. To improve ChOA from the perspective of multi-strategy mixing, MSChimp was proposed, and the algorithm was applied to global optimization and minimum spanning tree problems. The main research work of this paper is as follows: (1) In the initialization stage of ChOA, an opposition-based learning strategy was introduced to improve the population diversity; Sine Cosine Algorithm (SCA) was introduced in the exploitation process to improve the convergence speed and accuracy of the algorithm in the later stage, so as to balance the exploration and exploitation capabilities of the algorithm. (2) The improved algorithm was compared with different types of meta-heuristic algorithms in 20 benchmark functions and CEC 2019 test sets, and was used to solve the minimum spanning tree. The experimental results show that the improved ChOA has significantly improved the ability to find the optimal value, which verifies the effectiveness and feasibility of MSChimp. Compared with other algorithms, the algorithm proposed in this paper has strong competitiveness. [ABSTRACT FROM AUTHOR]

This paper presents an improved arithmetic optimization algorithm that incorporates hybrid elite pool strategies to address the limitations of the arithmetic optimization algorithm (AOA). In AOA, the linear mathematical optimization acceleration (MOA) function cannot balance global exploitation and local exploration well. Therefore, the accuracy and convergence speed of the algorithm cannot be guaranteed. To improve the performance of AOA, this paper reconstructed a nonlinear MOA function, which is expected to balance the exploitation and the exploration of AOA. Furthermore, four hybrid elite pool strategies are integrated to enhance the ability to escape local optima. The proposed algorithm inherits the fast convergence of AOA and develops the performance of escaping local optima. Numerical experiment results on benchmark functions and engineering problems show that the proposed algorithm outperforms other compared meta-heuristic algorithms in terms of convergence speed and accuracy. [ABSTRACT FROM AUTHOR]

Software quality is becoming a momentous challenge in software engineering processes, and software testing has a pivotal role in its measurements. Nature-inspired metaheuristic methods play an essential role in software testing, in which various studies have been conducted in this field; however, due to a lack of wide-ranging papers reviewing these methods, conducting a comprehensive systematic review to examine an array of crucial mechanisms in this field has become a necessity. This study aims to present a detailed analysis and taxonomically classifies the metaheuristic methods inspired by nature. This paper compromises a systematic literature review of 65 chosen studies published between 2015 and 2022. Genetic algorithm-based, hybrid, ant colony optimization-based, cuckoo search-based, firefly algorithm-based, artificial bee colony-based, and other metaheuristic methods constitute this systematic study's stratification. Evaluation methods, applied tools, merits, and demerits of each reviewed article are investigated. Additionally, future directions and open issues are addressed. This conducted paper not only expounds on software testing strengths, open issues, and future works, but also recognizes the quest for optimizing the insufficient metrics in software testing, such as mutation score, complexity, and scalability, which would be the propulsion of the testing process if consummated. [ABSTRACT FROM AUTHOR]

In order to solve the problems of insufficient optimization accuracy, slow convergence speed and easy to fall into local optimum in the whale optimization algorithm, this paper proposes a whale optimization algorithm with improved neighborhood search strategy. First, the algorithm generates a more evenly distributed and higher-quality initial population through an initialization strategy based on the opposite-based learning of pinhole imaging to expand the early search space of the algorithm. Secondly, it adopts improved neighborhood search strategy based on similarity and uses Mahalanobis distance and the law of universal gravitation to calculate and rank the similarity of solutions. At the same time, the algorithm counts the times of convergence oscillation. According to the algorithm iteration process, it selects the corresponding similarity ranking solution as the object to update the position and performs the second position update for the solution with most times of oscillation, so as to implement the space exploration of the target population to speed up the convergence of the algorithm and enhance the ability to jump out of the local optimum. Finally, an adaptive step size adjustment strategy is introduced, and the population convergence is adjusted using adaptive step size parameters according to the algorithm optimization process to improve the algorithm's global search performance and avoid premature convergence of the algorithm. The improved algorithm proposed in this paper is analyzed and compared with the sine–cosine optimization algorithm, artificial bee colony algorithm and three improved whale algorithms on a set of 20 test functions in low-dimensional and high-dimensional, respectively, and perform ANOVA and T-test on the simulation results. The results show that the improved algorithm proposed in this paper effectively improves the convergence accuracy and convergence speed. In addition, the improved optimization algorithm proposed in this paper is applied to the engineering optimization design. The solutions show that the improved algorithm can obtain the optimal value with higher accuracy and more stability than other algorithms, and can effectively solve the engineering design problem. [ABSTRACT FROM AUTHOR]

Optimization is necessary for solving and improving the solution of various complex problems. Every meta-heuristic algorithm can have a weak point, and multiple mechanisms and methods can be used to overcome these weaknesses. Some algorithms perform well in the discovery phase and some algorithms perform better in the exploitation phase. Hybridizing algorithms can be a good solution to achieve a powerful algorithm, and hybridizing algorithms and mechanisms greedily lead to an increase in computational complexity and execution time. This paper presents a new and intelligent approach by hybridizing meta-heuristic algorithms and using different mechanisms simultaneously without significantly increasing the time complexity. For this purpose, salp swarm optimization (SSO) and the African vulture optimization algorithm (AVOA) have been hybridized to improve the optimization process of the modified choice function and learning automata mechanisms. In addition, two other improving mechanisms, named opposition-based learning (OBL) and β-hill climbing (BHC) technique, have been presented and integrated with the AVOA–SSA algorithm. Fifty-two standard benchmarks were used to test and evaluate the AVOA–SSA algorithm. Finally, an improved version of the extreme learning machine (ELM) classifier has been used with real stock market data for stock market prediction. The obtained results indicate the excellent and acceptable performance of the AVOA–SSA algorithm in 'solving optimization problems and have been able to achieve high-quality solutions. According to the results obtained from the AVOA–SSA algorithm, in comparison to global optimization problems, the AVOA–SSA algorithm has been able to obtain the best results in 21 functions out of 23 standard benchmarks. Also, against CEC2017 problems, it has been able to perform best in 26 out of 29 functions. In addition, the AVOA–SSA algorithm has been able to perform better than other compared algorithms in all five datasets evaluated in the stock market. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) is an Intelligent Transportation System, which in its turn is an application of the Internet of Things. The IoV is a network of connected vehicles that sends/receives messages. However, the moving nature of the vehicles in IoV networks raises a dynamic topology issue. Therefore, establishing effective and dependable communication pathways among vehicular nodes, contingent upon traffic density conditions, presents a growing challenge. As a result, the main contribution of this paper is to propose an efficient network optimization system involving a cluster-based network routing optimizer. The proposed method is a metaheuristic algorithm adapted as a clustering algorithm. For this purpose, the JAYA Algorithm (JAYA) was utilized and integrated with two clustering concepts extracted from K-means Clustering and Automatic Clustering, resulting in proposing JAYA Clustering Algorithm for IoV (JAYACIoV). Subsequently, the performance of the proposed algorithm was investigated utilizing 176 network testing scenarios, and extensive comparative evaluations were conducted against five algorithms from the literature. The results confirmed the superiority of the proposed system with a percentage of 64.82%, where the algorithms were ranked according to the percentage achievement of best results. [ABSTRACT FROM AUTHOR]

This paper presents a method that employs an evolutionary normal distributions transform (NDT) for simultaneous localization and mapping (SLAM) using light detection and ranging (LiDAR) for autonomous mobile robots. An adaptive inertia weight and two genetic operators were employed in the Taguchi-based whale optimization algorithm (WOA) to improve the search diversity and avoid local optima. NDT was used to model the environment of differential-drive mobile robots and WOA was applied to optimize the scan matching for the robotic SLAM problem. The NDT-WOA determines the SLAM pose estimation using sensed data from the physical world. A nonholonomic mobile robot was steered to achieve the NDT-WOA SLAM task with the derived robot kinematics and actuator dynamics. The proposed method was implemented in a TurtleBot3 Burger robotic development kit, which includes a single-board computer and an OpenCR control board. The Robot Operating System (ROS) was utilized to implement the evolutionary NDT-WOA SLAM system due to its flexibility, open source, and client library. Simulation and comparisons were conducted to illustrate the efficiency of the proposed NDT-WOA SLAM method compared to other SLAM paradigms. The experimental results show the effectiveness of the proposed evolutionary NDT-WOA SLAM for autonomous mobile robots. The results could have theoretical and practical significance for robotics research. [ABSTRACT FROM AUTHOR]

Teaching–learning-based optimization (TLBO) algorithm is an excellent human-inspired optimization technique. This paper proposes an innovative improved version of TLBO—collectivism teaching–learning-based optimization (CTLBO) algorithm. This algorithm imitates group and individual behaviours in the reality of teaching and learning, applies group-individual multi-mode cooperation strategies to form new search mechanism. The CTLBO contains three phases, i.e. preparation phase, teaching and learning phases. In the preparation phase, there are two operators, i.e. teacher self-learning and teacher-learner interaction operators. In the teaching phase, class teaching and performance-based group teaching operators are implied. In the learning phase, neighbour learning, student self-learning and team-learning strategies are mixed together to form three operators. Two sets of experiments are conducted to test the performance of CTLBO. The first set of experiments validates the improvement effect of CTLBO by comparing it with the original TLBO and other authors' improved versions of TLBO. The second set of experiments illustrates the advantage of CTLBO by comparing it with other 17 meta-heuristic algorithms in solving 30 general benchmark functions and 15 CEC2015 test suit functions. The results of experiments show that CTLBO algorithm has significant improvement effect compared with TLBO. It is the most effective one amongst the improved versions of TLBO selected for comparison, and outperforms all other 17 meta-heuristic algorithms. The algorithm can significantly improve the convergence ability and the accuracy in solving different-scale complex optimization models. [ABSTRACT FROM AUTHOR]

Feature selection is an important component of the machine learning domain, which selects the ideal subset of characteristics relative to the target data by omitting irrelevant data. For a given number of features, there are 2n possible feature subsets, making it challenging to select the optimal set of features from a dataset via conventional feature selection approaches. We opted to investigate glaucoma infection since the number of individuals with this disease is rising quickly around the world. The goal of this study is to use the feature set (features derived from fundus images of benchmark datasets) to classify images into two classes (infected and normal) and to select the fewest features (feature selection) to achieve the best performance on various efficiency measuring metrics. In light of this, the paper implements and recommends a metaheuristics-based technique for feature selection based on emperor penguin optimization, bacterial foraging optimization, and proposes their hybrid algorithm. From the retinal fundus benchmark images, a total of 36 features were extracted. The proposed technique for selecting features minimizes the number of features while improving classification accuracy. Six machine learning classifiers classify on the basis of a smaller subset of features provided by these three optimization techniques. In addition to the execution time, eight statistically based performance metrics are calculated. The hybrid optimization technique combined with random forest achieves the highest accuracy, up to 0.95410. Because the proposed medical decision support system is effective and ensures trustworthy decision-making for glaucoma screening, it might be utilized by medical practitioners as a second opinion tool, as well as assist overworked expert ophthalmologists and prevent individuals from losing their eyesight. [ABSTRACT FROM AUTHOR]

It is well recognized that human societies and their problem-solving capabilities have evolved much faster than biological evolution. The inspiration from human behaviors, knowledge exchange, and transformation has given rise to a new evolutionary computation paradigm. Multiple research endeavors have been reported in the literature inspired by the diverse aspects of human societies with corresponding terminologies to describe the algorithm. These endeavors have resulted in piles of algorithms, worded differently but with more or less similar underlying mechanisms causing immense confusion for a new reader. This paper presents a generalized framework for these socio-inspired evolutionary algorithms (SIEAs) or socio-inspired meta-heuristic algorithms. A survey of various SIEAs is provided to highlight the working of these algorithms on a common framework, their variations and improved versions proposed in the literature, and their applications in various fields of search and optimization. The algorithmic description and the comparison of each SIEA with the general framework enable a clearer understanding of the similarities and differences between these methodologies. Efforts have been made to provide an extensive list of references with due context. This paper could become an excellent reference as a starting point for anyone interested in this fascinating field of research. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop and test three meta-heuristic algorithms to solve large size multi-objective supply chain network design problems. The algorithms are based on tabu search, genetic algorithm, and simulated annealing to find near-optimal global solutions. The three algorithms are designed, coded, and tested, and their parameters are fine tuned. The exact ε-constraint algorithm embedded in the General Algebraic Modeling System is used to validate the results of the three algorithms. The algorithms are compared using a typical multi-objective supply chain model utilizing several performance measures. The measures include the mean ideal distance, diversification metric, and percent of domination, inverted generational distance, and computation time. The results show that the tabu search algorithm outperformed the other two algorithms in terms of the percent of domination and computation time. On the other hand, the simulated annealing solutions are the best in terms of their diversity. The work in this paper is expected to help managers to solve large-scale supply chain problems that arise in oil and gas, petrochemical, and food supply chains. [ABSTRACT FROM AUTHOR]

Enhanced index tracking (EIT) problem is concerned with selecting a tracking portfolio to beat the benchmark on return while having the minimum tracking error. This paper addresses the EIT problem based on uncertainty theory where stock returns are treated as uncertain variables instead of random variables. Under the framework of uncertainty theory, the paper proposes a new uncertain EIT model where the higher-order moment of the downside is used as the tracking error measure, as higher-order moment makes the model more widely applicable and the downside risk is in line with investors' perception of risk. Besides, some realistic constraints are considered in the new uncertain EIT model. Then, the properties of the proposed model are discussed. To solve the model, we proposed, which is a nonlinear integer programming problem, a meta-heuristic algorithm presented. The efficiency of the algorithm and the applications of the proposed model are illustrated through numerical experiments. [ABSTRACT FROM AUTHOR]

Hybrid renewable power generation becomes essential in most of electric power networks. Battery storage is commonly used in renewable energy systems (RESs) with distributed generation, such as solar and wind energy systems, to reduce power fluctuations caused by the intermittent behavior of renewable energy sources. A battery has been connected with the dc grid through a dc–dc bi-directional converter. The control scheme of the dc–dc bi-directional converter (BDC) is having two controlled loops. One is voltage-controlled loop, and another is the current-controlled loop. In this work, two-degree-of-freedom fractional order proportional integral derivative + proportional integral controller (2-DOF FOPID + PI) is proposed and applied in the voltage-controlled loop to stabilize the dc bus voltage and power flow effectively for both islanded mode and grid-connected mode. A novel meta-heuristic algorithm, i.e., modified sine cosine algorithm (m-SCA), is used to tune the controller parameters of the proposed controller. To test the performance of the modified SCA algorithm, various well-known uni-modal and multi-modal benchmark functions are taken in this paper. To confer the viability of the proposed controller, its performance is compared with other literature-based conventional controllers. This paper presents time-domain analysis, stability, and robustness test to illustrate the effectiveness of the proposed controller. MATLAB/Simulink has been used for computational work to analyze the system's efficiency. Finally, the proposed technique is validated by using OPAL-RT platform (Opal-RT-OP4510 model) with the power management of RES (PV and wind) and battery energy storage. Experimental work proved the effectiveness of the proposed m-SCA optimized 2-DOF FOPID + PI controller. [ABSTRACT FROM AUTHOR]

A novel multi-layer multi-spiking neural network (MMSNN) model sends information from one neuron to the next through multiple synapses in different spikes. When a suitable training method is unavailable, there are numerous complications in training spike neural networks. The convergence rate is slow when gradient descent algorithms are trained on multi-spike neural networks. Meanwhile, the spiking network is likely to perform poorly in classifying electroencephalographic (EEG) datasets due to its high propensity to fall within the local minima. With the help of a 16-channel electrode, the Concealed Information Test created the EEG dataset. The Mini Batch Stochastic Gradient Descent (MBSGD) built the batch size and cut the number of iterations to speed up the convergence rate. It can, however, avoid the problem of moving into the local minimum more effectively. This paper describes an MBSGD algorithm for MMSNN model training that addresses local minima and convergence rate issues. This paper presents an MBSGD-MMSNN model for classifying EEG data. The MBSGD algorithm adjusts the weight parameters of the MMSNN model based on the batch size until the error is less than the required value. As a result, we achieve a faster convergence rate and avoid issues with local minima. The performance of the proposed MBSGD-MMSNN model is compared to that of existing meta-heuristic algorithms. The proposed MBSGD-MMSNN model outperformed other state-of-the-art algorithms regarding the accuracy and learning performance. [ABSTRACT FROM AUTHOR]

With the support of enterprise intelligence system, an intelligent modeling method based on synthesizing various data sources and complex metabolic networks is adopted to provide technical support for the practical application of complex large-scale dynamic models. Academic research on international trade networks relies to a large extent on network models based on macro-national data and static analysis of international trade patterns, which are usually based on charts for trade flows and economic globalization. This paper presents an improved neural network algorithm for analyzing the complex dynamic meta-network model of international trade. By using a BP neural network to adjust the weighted values of the best neural network model, the need for continuous adjustment of neural network values is reduced, which may be an effective means to improve the efficiency of intrusion testing applications. The simulation results demonstrate that the model has good explanatory power. The globalization of enterprise markets, as the main driver of international business activities, has a significant impact on the establishment and development of national or regional international trade networks. Therefore, there is a need to study international trade networks using data at the micro level. This paper suggests an interdisciplinary approach to the study of international trade networks and a micro-level study of international trade networks. The model based on complex meta-network dynamic model elements, introduce the dynamic network representation, and use temporary labels to define the network boundary characteristics. [ABSTRACT FROM AUTHOR]

This paper introduces the Golf Sport Inspired Search (GSIS) algorithm as an evolutionary search method for numerical optimization. Each solution is generated with the aid of the step-length and search direction. The step-length is determined with the aid of the Tait's model of the trajectory of the golf ball, which is a physical model. The search direction is from the current position in the search space toward the position of a different individual or its reflected position. Such a direction determines the movement direction in the optimization process. A crossover operator is introduced to increase exploration at the starting and exploitation at the ending stages of the search. Performance of the GSIS is compared with many algorithms on 23 + 14 unconstrained classic functions, 29 functions of CEC 2017 benchmark suite and six constrained engineering design problems. Experiments indicate that with the aid of its cleverly designed operators, GSIS is able to produce promising results. Besides a cooperative game theoretic approach is introduced, which is able to measure the effectiveness of different operators in reducing the search cost. Such an approach can be used to measure the effectiveness of different operators that an evolutionary or swarm-intelligence algorithm owns. [ABSTRACT FROM AUTHOR]

Production scheduling is a critical factor to enhancing productivity in manufacturing engineering and combinatorial optimization research. The complexity and dynamic nature of production systems necessitates innovative solutions. The Job Shop Flexible Programming Problem (FJSP) provides a realistic environment for production, where processing times are variable and uncertain, and multiple objectives need optimization. To solve the Multi-Objective Flexible Fuzzy Job Shop problem with partial flexibility (P-MOFfJSP), this paper proposes a hybrid metaheuristic approach that combines the Teaching–Learning-based Optimization (TLBO) algorithm with a Genetic Algorithm. The proposed algorithm of Adaptive TLBO (TLBO-A) uses two genetic operators (mutation and crossover) with an adaptive population reconfiguration strategy, ensuring solution space exploration and preventing premature convergence. We have evaluated the TLBO-A algorithm's performance on benchmark instances commonly used in programming problems with fuzzy variables. The experimental analysis indicates significant results, demonstrating that the adaptive strategy improves the search for suitable solutions. The proposed algorithm (TLBO-A) exhibits low variations (around 11%) compared to the best mono-objective heuristic for the fuzzy makespan problem, indicating its robustness. Moreover, compared with other heuristics like traditional TLBO, the variations decrease to around 1%. However, TLBO-A stands out as it aims to solve a multi-objective problem, improving the fuzzy makespan, and identifying good results on the Pareto frontier for the fuzzy average flow time, all within this low variation margin. Our contribution addresses the challenges of production scheduling in fuzzy time environments and proposes a practical hybrid metaheuristic approach. The TLBO-A algorithm shows promising results in solving the P-MOFfJSP, highlighting the potential of our proposed methodology for solving real-world production scheduling problems. [ABSTRACT FROM AUTHOR]

This paper aims to address uncertainty in practical applications that the experimental observations and measurements have nature of inhomogeneity, randomness, and imprecision. This study employs a new method for regression model prediction in an uncertain environment and presents fuzzy parameter estimation of fuzzy regression models using triangular fuzzy numbers. These estimation methods are obtained by new learning algorithms in which linear programming is used. In this study, the new algorithm is a combination of a fuzzy rule-based system, on the basis of particle swarm optimization (PSO) and ant colony optimization ACOR. In the adaptive neuro-fuzzy inference system method, the output is crisp. In this research, a hybrid algorithm has been proposed to get the fuzzy output based on linear programming. Meta-heuristic algorithms have been used to reduce the error in the proposed methods based on PSO and ACOR. In addition, two simulation and two practical examples in the field of machining process are applied to indicate the performance of the proposed methods in dealing with crisp input and fuzzy output (CIFO) problems where the observed output variables have the nature of inhomogeneity, randomness, and imprecision. Finally, the results of the proposed algorithms are evaluated. Based on examples, the proposed method is more accurate than the LP and FWLP methods but is not more complicated than the FWLP in computations. Using paired t test, a significant difference was shown between the proposed methods and previous methods, such as LP and FWLP, but there was no significant difference between the two proposed methods ACOR and PSO. [ABSTRACT FROM AUTHOR]

This paper addresses an integrated material flow optimization problem of cross-docking satellites, in which the transportation problem, the truck-door assignment problem with material placement plans, and the two-dimensional truck loading problem are taken into account. The study aims to find the best distribution and operational plans for the cross-docking satellites to minimize the total transportation cost of the materials. To solve the considered problem, a hybrid genetic algorithm (HGA) is developed, which integrates simulated annealing (SA) algorithm within a genetic algorithm (GA). In this way, a new individual with a low solution quality is rejected by using the stochastic solution acceptance feature of the SA. Moreover, the HGA is enhanced with an advanced two-dimensional loading-check procedure and a rule-based material placement procedure to obtain efficient solutions. The proposed loading-check procedure reduces the processing time of the HGA by avoiding duplicate examinations for the truck loading plans. Likewise, the rule-based material placement procedure prevents unnecessary searches for the assignment plans of the products in a temporary storage area. In computational studies, the performance of the HGA is tested on two different problem sets by comparing HGA with the SA and GA. Furthermore, the HGA is applied to a problem set that is formed by using real-life data of a logistics company. The computational results show that the HGA introduces effective solutions and outperforms both the SA and GA. In addition, the results of the real-life application denote that the HGA can be employed to find effective material flow plans in real situations of cross-docking operations. [ABSTRACT FROM AUTHOR]

With the proliferation in the internet of things (IoT), a variety of delay sensitive applications have emerged over the past few years. Fog computing acts a viable computing paradigm for meeting the requirements of IoT applications. However, heterogeneous IoT tasks, resource constrained fog nodes makes task scheduling a challenging task. Efficient task scheduling reduces the computation cost, latency, energy and enhances the utilization of resources. In this paper, we propose the improved version of the meta-heuristic algorithm spider monkey optimization (SMO). We propose greedy task scheduling SMO (gTS-SMO) and semi-greedy task scheduling SMO (sgTS-SMO) for efficient task scheduling in fog computing environment. The main aim is minimize delay and energy consumption while considering the constraints of deadline and violation time. The system is evaluated based on the parameters of deadline violation time, makespan, energy consumption, total cost, and degree of imbalance. The results shows gTS-SMO reduces the violation time by 13.86%, 88.38% on comparison with sgTS-SMO and particle swarm optimization (PSO) respectively. The results also depicts the outperformance of gTS-SMO in terms of makespan and energy consumption. Makespan is reduced by 6.28%, and 57.75% and energy consumption by 5.74% and 53.65% on comparison with sgTS-SMO and PSO respectively. [ABSTRACT FROM AUTHOR]

Recently, image thresholding methods based on various entropy functions have been found popularity. Nonetheless, entropic-based methods depend on the spatial distribution of the grey level values in an image. Hence, the accuracy of these methods is limited due to the non-uniform distribution of the grey values. Further, the analysis of the COVID-19 X-ray images is evolved as an important area of research. Therefore, it is needed to develop an efficient method for the segmentation of the COVID-19 X-ray images. To address these issues, an efficient non-entropy-based thresholding method is suggested. A novel fitness function in terms of the segmentation score (SS) is introduced, which is used to reduce the segmentation error. A soft computing approach is suggested. An efficient optimizer using the chance-based birds' intelligence is introduced to maximize the fitness values. The new optimizer is validated utilizing the benchmark test functions. The statistical parameters reveal that the suggested optimizer is efficient. It shows a quite significant improvement over its counterparts—optimization based on seagull/cuckoo birds. Precisely, the paper includes three novel contributions—(i) fitness function, (ii) chance-based birds' intelligence for optimization, (iii) multiclass segmentation. The COVID-19 X-ray images are taken from the Kaggle Radiography database, to the experiment. Its results are compared with three different state-of-the-art entropy-based techniques—Tsallis, Kapur's, and Masi. For providing a statistical analysis, Friedman's mean rank test is conducted and our method Ranked one. Its superiority is claimed in terms of Peak Signal to Noise Ratio (PSNR), Feature Similarity Index (FSIM) and Structure Similarity Index (SSIM). On the whole, an improvement of about 11% in PSNR values is achieved using the proposed method. This method would be helpful for medical image analysis. [ABSTRACT FROM AUTHOR]

In recent years, the resource-constrained project scheduling problem and its variants have attracted wide attention from the perspective of theory and practice. In many projects, the amounts of the work content for the activities are specified, while the activities are executed in different modes of discrete duration and resource consumption per time. This paper focuses on this specific generalization of the resource-constrained project scheduling problem, known as the discrete time/resource trade-off problem (DTRTP). An efficient mathematical model for the DTRTP with renewable resource types is presented. Since this problem is NP-hard, a hybrid heuristic/meta-heuristic algorithm is proposed to solve the deterministic model in large sizes. Then, a critical chain project management approach is employed to handle the uncertainty of activities' work contents. Finally, several numerical examples based on the previous studies and generated examples are presented to demonstrate the performance of the proposed procedure. The proposed hybrid algorithm for deterministic cases is statistically compared with an existing exact optimization tool. The simulation-based statistical analyses showed that the proposed hybrid meta-heuristic algorithm could find global optimums for small-sized cases in shorter run times. While the exact solver cannot solve medium- and large-sized problems, the proposed nested algorithm reaches high-quality local solutions in suitable run times. Also, the simulations indicated that the proposed project scheduling can face uncertainty, at least in 77% of the cases. [ABSTRACT FROM AUTHOR]

This paper presents an effective approach for solving economic load dispatch problems contemplating the scheduling a set of thermal generating units to produce a specific power at low consumption costs. These problems can be thought of as nonlinear, non-convex, and highly constrained optimization problems with a large number of local minima. To cope with the above issues in solving such problems, a new meta-heuristic named capuchin search algorithm was adopted. To boost the search performance of this algorithm as well as to mitigate its early convergence and regression to the local optimum, it was hybridized with another algorithm and improved using several positive amendments. First, a memory element was added to this algorithm to ameliorate its position and velocity update mechanisms in order to exploit the most encouraging candidate solutions. Second, two adaptive parametric functions were used to manage the exploration and exploitation features of this algorithm and balance them appropriately. Finally, the hybridization was made using the gradient-based optimizer to strengthen the intensification ability of this algorithm and balance its searching ability to fulfill sensible search performance. The proficiency of the proposed algorithm was divulged by assessing it on computationally difficult economic load dispatch problems under 6 different tests with a generator of 3, 13, 40, 80, and 140 units, each with different constraints and load conditions. The proposed algorithm provided the best performance among many other competitors. Its superiority and practicality were revealed by obtaining optimal solutions for large-scale test cases such as 40-unit and 140-unit test systems. [ABSTRACT FROM AUTHOR]

In order to solve optimization problems, this paper introduces a new metaheuristic algorithm called the Numeric Crunch Algorithm (NCA), which employs the distribution behaviour of the population members and a novel hyperbolic acceleration function for convergence. Each generation's search space exploration and exploitation are ensured by the population's distribution behaviour around its members and their adaptively diversified boundaries, respectively. The convergence of the search solutions in the NCA was also accelerated by the random, adaptive parameters and hyperbolic function. A set of 68 test benchmark functions with (30, 100, 500, and 1000) dimensions was used to examine the NCA's effectiveness in terms of exploration, exploitation, local optimality avoidance, population fitness enhancement, and convergence rate. Firstly, the proposed NCA's behaviour is examined using a collection of 23 standard well-known benchmark functions, including unimodal, multimodal, and fixed-dimensional functions. Secondly, the proposed NCA's superiority is examined using the IEEE CEC-2015 and IEEE CEC-2017 benchmark suites. In addition to qualitatively examine NCA's superiority over other metaheuristics, Friedman and Wilcoxon rank-sum tests are performed. In terms of performance metrics, NCA ranked first. For application perspective, the NCA is tested on eight real-world constrained and unconstrained engineering design problems from IEEE CEC-2020 real-world optimization benchmark suits. The NCA algorithm's performance on benchmark functions and engineering design problems indicates that it can handle constrained and uncertain search spaces in real-world scenarios. The source code of the NCA algorithm is publicly available at https://github.com/Shivankur07/Numeric-Crunch-Algorithm.git. [ABSTRACT FROM AUTHOR]

During the optimization process, a large number of data are generated through the search. Machine learning techniques and algorithms can be used to handle the generated data to contribute to the optimization process. The use of machine learning enhancing metaheuristics applied to optimization problems has been drawing attention due to their capacity to add domain knowledge during the search process. This knowledge can accelerate metaheuristics and lead to better and promising solutions. This work provides a systematic literature review of machine learning enhancing metaheuristics and summarizes the current state of the classification of the research field, main techniques and machine learning models, validations strategies, and real-world optimization problems that the approach was applied. Our keyword search found 1.960 papers, published in the last 10 years. After considering the inclusion and exclusion criteria and performing backward snowballing procedure, we have analyzed 111 primary studies. The results show the predominance of the use of surrogate-assisted evolutionary algorithms (SAEAs) for improving the efficiency of the optimization, and the use of estimation of distribution algorithms (EDAs) to increase the effectiveness of the optimization. The objective function value is the mostly applied evaluating criteria to validate the algorithm with other methods. The developed techniques of the studies found are applied in diverse real-world applications such as developing machine learning models, physics simulations with expensive function evaluation, and the variants of the classical job shop scheduling problem. We also discuss trends and opportunities of the research field. [ABSTRACT FROM AUTHOR]

Meta-heuristics have been successfully applied to many complex optimization problems. One of the main reasons for its success is its ability to handle non-convex, nonlinear, multimodal, multi-variable, and multi-objective problems with easy implementation. However, the quality of the response of these algorithms to an optimization problem is highly susceptible to the control parameters, and few works aim to tune them or find tools that can improve the algorithms. The literature is rich in proposals for new algorithms, but not for improving existing ones. This paper presents different strategies for tuning and accelerating meta-heuristics using the first hybrid algorithm in the literature. The Lichtenberg algorithm is inspired by lightning and Lichtenberg figures and has been increasingly successfully applied to various optimization problems. However, a study of its best parameters has never been presented until now. After a discussion of the best tuning tools, its tuning parameters are performed using response surface methodology. Then, 14 versions are studied through 10 test functions using chaos theory and Lévy flights scenarios. After 13,500 simulations, the chaotic Lichtenberg algorithm equipped with the piecewise function and tuned parameters proved the best version with only 16% similarity to the original algorithm. Then, it was compared to the genetic algorithm, particle swarm optimization, gray wolf optimizer, salp swarm optimization, whale optimization algorithm, and dragonfly algorithm. The proposed algorithm had both the best average accuracy, lower computational cost, and the smallest standard deviation. [ABSTRACT FROM AUTHOR]

Due to the users' mobility, new online applications, as well as low processing power and limited energy of smart devices, traditional cloud computing models could not provide new required services. Cloud service providers improve the quality of their services by moving some servers to the edge of the network and closer to mobile users. Considering the moving nature of users and the heterogeneous service demands in different areas, the optimal placement of servers plays an important role in increasing the quality of service provided to users. However, because of the large number of servers, finding the optimal location of these resources is a serious challenge. In the proposed method of this paper (MES-WG), in the first step, the geographical area of server deployment is divided into smaller sub-regions to reduce the complexity of the problem. Then, by using the WOA algorithm the search agent finds the optimal location of the servers. In the next step, a neural network is used for the local placement of all servers in each area. Finally, game theory is deployed for the convergence of resource placement in all sub-regions. The experimental results show that the proposed method reduces the network latency by 33.5% and also improves the load balance on servers by 28.2%, compared to some of the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This study explores the applicability of the metaheuristic algorithm for calculating the building period. Firefly algorithm (FA) employing the feed-forward (FF) model is applied to an existing period dataset of several wood buildings, whose motions were recorded at ambient conditions. The composition of the considered artificial neural network (ANN) is optimized employing the firefly algorithm, and the fixed weights are determined with the least error for the model. The model's precision is evaluated by comparing the results with the multiple linear regression (MLR) model, the ANN model combined with the genetic algorithm (GA-ANN), particle swarm optimization algorithm (PSO-ANN), and the models existing in building code. The results are also compared with the models presented in the literature for the period estimate required for the seismic design of wood buildings. The paper concludes that the proposed informational model can predict the fundamental period of light-frame wood buildings more accurately than the other available models. [ABSTRACT FROM AUTHOR]

Real-parameter single objective optimization has been studied for decades. In recent, a new setting is applied in this field based on the consideration that solving difficulty scales exponentially with the increase in dimensionality. Under the new setting, differential evolution (DE) still outstands in performance as before. Meanwhile, a new type of population-based metaheuristic—gaining–sharing knowledge-based algorithm, becomes a dark horse. Furthermore, ensemble of the above two types of algorithm is proposed in the literature. Although such ensemble shows good performance, provided that a more reasonable scheme is used for the communication between the constituent algorithms, better ensemble can be obtained. We believe that the new scheme should be with adaptiveness. In this paper, we propose an adaptive scheme for the communication. According to the scheme, individuals chosen based on fitness and lifetime are exchanged. In fact, in the field of DE, it is rare to consider lifetime of individual. However, lifetime is no less important than fitness in our scheme. In our experiment, our ensemble is compared with seven state-of-the-art algorithms. According to experimental results, our ensemble is comparable to one of the peers and better than the other ones. [ABSTRACT FROM AUTHOR]

For multi-threshold segmentation of grayscale and color images, the computational complexity increases exponentially with the increase of the number of threshold levels. In this paper, we propose a new method to segment grayscale and color images with Kapur entropy as the objective function. The method introduces four strategies such as horizontal crossover and vertical crossover into the bald eagle search optimization algorithm, forming an advanced bald eagle search optimization algorithm (ABES). In the benchmark function comparison experiments at IEEE CEC 2017, ABES was compared with classical and novel algorithms, and the results proved to have stronger convergence speed, convergence accuracy, and stability than other algorithms. To demonstrate the effectiveness of the method in multi-thresholding segmentation of grayscale and color images, it is applied to low-level and high-level image multi-thresholding experiments, and the experimental results show that ABES outperforms other algorithms in the evaluation of PSNR, MSSIM, and FSIM, and ABES is a high-quality image segmentation method. [ABSTRACT FROM AUTHOR]

The rapid growth of data generated by several applications like engineering, biotechnology, energy, and others has become a crucial challenge in the high dimensional data mining. The large amounts of data, especially those with high dimensions, may contain many irrelevant, redundant, or noisy features, which may negatively affect the accuracy and efficiency of the industrial data mining process. Recently, several meta-heuristic optimization algorithms have been utilized to evolve feature selection techniques for dealing with the vast dimensionality problem. Despite optimization algorithms' ability to find the near-optimal feature subset of the search space, they still face some global optimization challenges. This paper proposes an improved version of the sooty tern optimization (ST) algorithm, namely the ST-AL method, to improve the search performance for high-dimensional industrial optimization problems. ST-AL method is developed by boosting the performance of STOA by applying four strategies. The first strategy is the use of a control randomization parameters that ensure the balance between the exploration–exploitation stages during the search process; moreover, it avoids falling into local optimums. The second strategy entails the creation of a new exploration phase based on the Ant lion (AL) algorithm. The third strategy is improving the STOA exploitation phase by modifying the main equation of position updating. Finally, the greedy selection is used to ignore the poor generated population and keeps it from diverging from the existing promising regions. To evaluate the performance of the proposed ST-AL algorithm, it has been employed as a global optimization method to discover the optimal value of ten CEC2020 benchmark functions. Also, it has been applied as a feature selection approach on 16 benchmark datasets in the UCI repository and compared with seven well-known optimization feature selection methods. The experimental results reveal the superiority of the proposed algorithm in avoiding local minima and increasing the convergence rate. The experimental result are compared with state-of-the-art algorithms, i.e., ALO, STOA, PSO, GWO, HHO, MFO, and MPA and found that the mean accuracy achieved is in range 0.94–1.00. [ABSTRACT FROM AUTHOR]

Contrast enhancement is an important pre-processing task for several image and video processing applications. The objective of a contrast enhancement method is to improve the quality of the visual information contained in the images for further processing. Due to the enormous challenges, it is still considered as an open research problem. Several approaches have been proposed in the literature based on spatial and fr uency domain techniques. Among them, the Incomplete Beta Function (IBF) is a popular scheme used for image contrast enhancement. In the IBF based image contrast enhancement technique, quality of an image is improved by two controlling parameters. Under such conditions, these parameters need to be tuned for obtaining better outcomes. In this paper, a new gray-scale contrast enhancement algorithm is introduced where, instead of tuning the controlling parameters of IBF experimentally, their near-optimal values are calculated with the help of a recently published meta-heuristic algorithm called Artificial Electric Field Algorithm (AEFA). The proposed method has been compared with many state-of-the-art techniques in terms of some standard metrics considering three different datasets, namely Kodak, MIT-Adobe FiveK and USC-SIPI. Simulation results demonstrate that the proposed AEFA based image enhancement technique increases the overall image contrast and enriches the information present in the image. [ABSTRACT FROM AUTHOR]

Disasters are large-scale disruptions for the society that cause damage to human lives, infrastructure, and the ecosystem. Multiple emergencies emerge, which need to be handled and resolved efficiently to minimize the impact of the disaster. Therefore, the need for an effective and efficient emergency response system that optimally allocates resources and emergency services is essential. This paper proposes a methodology to model this situation as an optimization problem that can be solved using meta-heuristic algorithms. In the proposed model, four meta-heuristics, namely Particle Swarm Optimization, Cuckoo Search, Grey Wolf Optimizer, and iLSHADE-RSP algorithm, have been used to allocate resources and services. A benchmark dataset consisting of 16 situations is prepared to analyze the proposed model. The conducted empirical analysis demonstrates the applicability of the meta-heuristic algorithms in locating near-optimum solutions for the considered situations. The convergence analysis and statistical tests have been performed to test the validity and significance of the conducted experiments. [ABSTRACT FROM AUTHOR]

Cooperation search algorithm (CSA) is a new metaheuristic algorithm inspired from the team cooperation behaviors in modern enterprises and is characterized by fast convergence. However, for complex multimodal problems, it may get trapped into local optima and suffer from premature convergence for the shortcoming of population updating guided only by leading individuals. In this paper, the issue of low convergence efficiency and convergence accuracy of the CSA algorithm on complex multimodal problems is dramatically alleviated by integrating the mutation and crossover operators in DE algorithm. Experimental results demonstrate the better performance of CCSA on convergence speed and accuracy as compared to other existing optimizers. Furthermore, aiming at the problem that there is no universal approach for the multi-degree reduction in Ball Bézier surfaces under different interpolation constrains, we propose a new method to solve this problem by introducing metaheuristic methods, where the change of interpolation constrains is treated as the change of decision variables. The modeling examples show that the proposed method is effective and easy to implement under different interpolation constrains, which can achieve the multi-degree reduction in Ball Bézier surfaces at one time and can simplify the degree reduction procedure significantly. [ABSTRACT FROM AUTHOR]

This paper introduces a new meta-heuristic technique, named geometric mean optimizer (GMO) that emulates the unique properties of the geometric mean operator in mathematics. This operator can simultaneously evaluate the fitness and diversity of the search agents in the search space. In GMO, the geometric mean of the scaled objective values of a certain agent's opposites is assigned to that agent as its weight representing its overall eligibility to guide the other agents in the search process when solving an optimization problem. Furthermore, the GMO has no parameter to tune, contributing its results to be highly reliable. The competence of the GMO in solving optimization problems is verified via implementation on 52 standard benchmark test problems including 23 classical test functions, 29 CEC2017 test functions as well as nine constrained engineering problems. The results presented by the GMO are then compared with those offered by several newly proposed and popular meta-heuristic algorithms. The results demonstrate that the GMO significantly outperforms its competitors on a vast range of the problems. Source codes of GMO are publicly available at https://github.com/farshad-rezaei1/GMO. [ABSTRACT FROM AUTHOR]

Archimedes optimization algorithm (AOA) is a new meta-heuristic algorithm which is based on Archimedes principle and mimics the buoyancy force received by an object in water. The AOA is designed according to physical principles and has been the object of many scholars' research because of its simple and reliable performance. In the course of the study, this paper finds that the AOA is flawed. In the iterative update of the algorithm, the buoyancy principle applied to the object is not completely followed. Through the investigation and analysis of this problem, it is found that the algorithm design which follows the buoyancy principle completely is more advantageously and persuasively, and named the corrected algorithm CAOA. The performance of the CAOA and other comparison optimization algorithms is tested in benchmark functions CEC2017 under equal conditions to verify the ideas proposed in this paper. In the solution accuracy with dimensions of 30 and 50, the comprehensive score of the CAOA is 31 and 33 and ranks first in all algorithms. In the statistical analysis, the CAOA compared with other algorithms one by one, and achieved the best results in all test functions. When compared with other algorithms, the CAOA ranked first. It is hoped that the verification of the ideas in this paper will help the AOA to develop better and optimize the development of the algorithm. [ABSTRACT FROM AUTHOR]

Human facial analysis (HFA) has recently become an attractive topic for computer vision research due to technological progress and mobile applications. HFA explores several issues as gender recognition (GR), facial expression, age, and race recognition for automatically understanding social life. This study explores HFA from the angle of recognizing a person's gender from their face. Several hard challenges are provoked, such as illumination, occlusion, facial emotions, quality, and angle of capture by cameras, making gender recognition more difficult for machines. The Archimedes optimization algorithm (AOA) was recently designed as a metaheuristic-based population optimization method, inspired by the Archimedes theory's physical notion. Compared to other swarm algorithms in the realm of optimization, this method promotes a good balance between exploration and exploitation. The convergence area is increased By incorporating extra data into the solution, such as volume and density. Because of the preceding benefits of AOA and the fact that it has not been used to choose the best area of the face, we propose utilizing a wrapper feature selection technique, which is a real motivation in the field of computer vision and machine learning. The paper's primary purpose is to automatically determine the optimal face area using AOA to recognize the gender of a human person categorized by two classes (Men and women). In this paper, the facial image is divided into several subregions (blocks), where each area provides a vector of characteristics using one method from handcrafted techniques as the local binary pattern (LBP), histogram-oriented gradient (HOG), or gray-level co-occurrence matrix (GLCM). Two experiments assess the proposed method (AOA): The first employs two benchmarking datasets: the Georgia Tech Face dataset (GT) and the Brazilian FEI dataset. The second experiment represents a more challenging large dataset that uses Gallagher's uncontrolled dataset. The experimental results show the good performance of AOA compared to other recent and competitive optimizers for all datasets. In terms of accuracy, the AOA-based LBP outperforms the state-of-the-art deep convolutional neural network (CNN) with 96.08% for the Gallagher's dataset. [ABSTRACT FROM AUTHOR]

Automotive Open System Architecture (AUTOSAR), as an open, standardized framework for automotive electronic software development, has gradually become the standard followed by major automotive manufacturers and automotive electronic device suppliers. The electronic software system problem improves the development efficiency and portability of the system by reducing the development cost of automotive electronic software while ensuring the quality of products and services, which is beneficial for subsequent upgrades and updates of the system. In order to improve the reliability of the software component deployment algorithm based on AUTOSAR architecture, we proposed the TDCA algorithm. During the execution of the algorithm, communication volume and communication degree are introduced to improve the accuracy of the deployment plan by optimizing the bus load and ECU balancing. Algorithm comparison experiments show that comparing heuristic and linear optimization algorithms, the TDCA algorithm proposed in this paper has significant advantages in reducing bus load and ECU utilization. The algorithm can reduce the communication between cores and balance ECU load according to the constraints of AUTOSAR architecture. [ABSTRACT FROM AUTHOR]

Community detection is a prominent research topic in Complex Network Analysis, and it constitutes an important research field on all those areas where complex networks represent a powerful interpretation tool for describing and understanding systems involved in neuroscience, biology, social science, economy, and many others. A challenging approach to uncover the community structure in complex network, and then revealing the internal organization of nodes, is Modularity optimization. In this research paper, we present an immune optimization algorithm (opt-IA) developed to detect community structures, with the main aim to maximize the modularity produced by the discovered communities. In order to assess the performance of opt-IA, we compared it with an overall of 20 heuristics and metaheuristics, among which one Hyper-Heuristic method, using social and biological complex networks as data set. Unlike these algorithms, opt-IA is entirely based on a fully random search process, which in turn is combined with purely stochastic operators. According to the obtained outcomes, opt-IA shows strictly better performances than almost all heuristics and metaheuristics to which it was compared; whilst it turns out to be comparable with the Hyper-Heuristic method. Overall, it can be claimed that opt-IA, even if driven by a purely random process, proves to be reliable and with efficient performance. Furthermore, to prove the latter claim, a sensitivity analysis of the functionality was conducted, using the classic metrics NMI, ARI and NVI. [ABSTRACT FROM AUTHOR]

In this paper, a novel metaheuristic optimization algorithm, called chief executive officer election optimization algorithm (CEOA), is proposed, which is inspired by the process of electing a Chief Executive Officer (CEO) in a company. CEOA simulates three stages of electing a CEO, namely the mass-election stage, election stage and authorization stage. The list of candidates is confirmed during the mass-election stage. In the election stage, each candidate has its own public relation team, and all the employees are divided into three different groups. The loyalist group always supports the candidate of their own faction, which enhances the exploitation ability of the algorithm. The speculator group tries to seize the CEO's position directly by taking advantage of the current candidates through a variety of behaviors, which enhance the exploration ability of the algorithm. The neutralist group takes its own interests as the priority, supports the candidate who meet its own interests by comparing the candidate teams and improves the exploitation and exploration ability of the algorithm at the same time. The final CEO is confirmed in the authorization stage. In addition, a precocity judgment rule is introduced to ensure the effectiveness and rationality of the election process and improve the ability of the algorithm to escape from the local optimal region. The performance of CEOA is evaluated through twenty-one classical test functions, twenty-nine CEC2017 test functions, six real-world engineering optimization problems and three date clustering problems. The average value, standard deviation, Friedman mean rank and Wilcoxon signed rank are used as criteria. The above experiments are compared with the well-studied and recent optimizers, such as GA, PSO, DE, EO, MPA, AEFA, SHADE, ISOS, mSSA and PO. For the twenty-one classical test functions, in both cases of function shifting and without shifting, CEOA can gain the first rank, and its performance in high-dimensional search space also outperforms other algorithms. For the twenty-nine CEC2017 test functions, CEOA gain the third rank, only slightly behind SHADE and LSHADE-SPACMA. However, in the twenty-one classical test functions without shifting, Friedman mean rank of CEOA is 2.4762, while SHADE and LSHADE-SPACMA are only 4.5952 and 6.0476, respectively. The experimental results show that CEOA outperforms most of optimizers and is competitive compared with high-performance methods. In addition, CEOA ranks first on all six constrained engineering optimization problems and has good performance on three real-life datasets clustering problems, proving its applicability on real-world optimization problems. [ABSTRACT FROM AUTHOR]

In the past few decades, meta-heuristic algorithms have become a research hotspot in the field of evolutionary computing. The electric fish optimization algorithm (EFO) is a new meta-heuristic algorithm. Because of its simplicity and easy implementation, it has attracted the attention of researchers. However, it still faces premature convergence and poor balance between exploration and exploitation. To address this problems, an orthogonal electric fish optimization algorithm with quantization (QOXEFO) is proposed in this paper. In QOXEFO, orthogonal cross-design and quantification technique are employed to enhance the diversity of population and convergence precision of EFO. Secondly, the dynamic boundary mechanism is adopted to improve the convergence speed of EFO. At the same time, a sine-based update strategy of active electrolocation is used to change the direction of movement of individuals, thereby helping them jump out of the local optimum. Finally, the CEC2017 benchmark function and Speed reducer design problem are used to verify the performance of the proposed QOXEFO. Experimental results and statistical analysis show that compared with 9 famous evolutionary algorithms, QOXEFO is competitive in solution accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

To overcome the shortcomings of the traditional parameter identification methods of photovoltaic cell model, including low accuracy, slow convergence speed, easy to be trapped in local optimum so on, a parameter identification method of photovoltaic cell model based on improved elephant herding optimization algorithm is proposed in this paper. The fast-moving operator is used, which greatly improves the convergence speed and global searching ability of the algorithm. The elitist strategy is also introduced where the worst individual is replaced with the optimal individual to improve the convergence speed and shorten the optimization time of the algorithm. When applied to the parameter identification of the photovoltaic cell model, the identification result of the improved elephant herding optimization algorithm proves to be better than other algorithms. The parameter identification of the photovoltaic cell model under different radiation conditions is carried out, and the fitting result of identification data is highly consistent with the measured data. Experiments validate that the improved elephant herding optimization algorithm can accurately and effectively identify the parameters of the photovoltaic cell model in different environments. [ABSTRACT FROM AUTHOR]

In recent years, the Internet of Things (IoT) has led to the spread of cloud computing devices in all commercial, industrial and agricultural sectors. The use of cloud computing environment services is increasing exponentially with all technology applications based on IoT. Fog computing has led to addressing issues in cloud computing environments. Fog computing reduces load balancing, processing, bandwidth, and storage as data file replication from the cloud to the network closest to sensors in different geographic locations. There are three critical issues:—what data should be replicated?—when should the data be replicated? and—where the new replicas should be placed? These three main open questions must be tackled for data replication in the cloud environments. This strategy, the identification and mode of the data replication problem are designed as a multi-objective optimization with modern meta-heuristic optimization method. Therefore, a new hybrid method using Arithmetic Optimization Algorithm (AOA) and the salp swarm algorithm (SSA) is proposed in this paper. Firstly, a new hybrid metaheuristic method, using the Arithmetic Optimization Algorithm (AOA) and the salp swarm algorithm (SSA), is proposed to handle the problem of selection and placement data replication in fog computing. Secondly, the Floyd algorithm is used to strategy the least cost path, distance, and data transmission in different geographic locations. The performance of the designed AOASSA strategy to tackling the data replication problem is evaluated using different datasets of different sizes. To validate the AOASSA strategy, a set of experiments was carried out to validate the proposed strategy AOASSA. Experiment results show the superiority of AOASSA over its competitors in terms of performance measures, such as least cost path, distance, and bandwidth. [ABSTRACT FROM AUTHOR]

In this paper, a new U-shaped assembly line balancing problem is studied. For the first time, the criteria such as equipment cost, number of stations and activity performing quality level are considered to be optimized simultaneously by activity to station and worker to station decisions. For this aim, a multi-objective nonlinear formulation is proposed and its linearized version is also presented. Since, according to the literature, the U-shaped assembly line balancing problem with equipment requirements is an NP-hard problem, the problem of this study is NP-hard too. Because of this complexity, the classical algorithms like simulated annealing, variable neighborhood search, and classical genetic algorithm with a novel encoding/decoding scheme are used as solution approaches. As an extension, two hybrid versions of the proposed classical algorithms are proposed according to the characteristics of the problem. In order to evaluate the proposed meta-heuristics, because the problem is new, some test problems are generated randomly. Computational study of the paper, including sensitivity analysis of the proposed meta-heuristics and final experiments on the test problems, proves the superiority of the hybrid versions of the classical algorithms. [ABSTRACT FROM AUTHOR]

Butterfly Optimization Algorithm (BOA) is a recent metaheuristic that has been used in several optimization problems. In this paper, we propose a new version of the algorithm (xBOA) based on the crossover operator and compare its results to the original BOA and 3 other variants recently introduced in the literature. We also proposed a framework for solving the unknown area exploration problem with energy constraints using metaheuristics in both single- and multi-robot scenarios. This framework allowed us to benchmark the performances of different metaheuristics for the robotics exploration problem. We conducted several experiments to validate this framework and used it to compare the effectiveness of xBOA with well-known metaheuristics used in the literature through 5 evaluation criteria. Although BOA and xBOA are not optimal in all these criteria, we found that BOA can be a good alternative to many metaheuristics in terms of the exploration time, while xBOA is more robust to local optima; has better fitness convergence; and achieves better exploration rates than the original BOA and its other variants. [ABSTRACT FROM AUTHOR]

The concepts of generalized Hukuhara difference and interval differential equation play important role in the theory of interval uncertainty. These concepts have many applications in different branches of research, viz. optimization, information theory, inventory control and others. The goal of this work is to study an application of Hukuhara difference and interval differential equation in inventory management. In this paper, an inventory model for imperfect production process under warranty-dependent demand and carbon tax regulatory mechanism, is presented with the help of Hukuhara difference and interval differential equation. Also, using the interval arithmetic, the generalized Hukuhara difference, and the existence and uniqueness theorem of interval differential equation, the corresponding average profit function of this model is obtained. In order to maximize the average profit, a center-radius optimization technique is proposed. Some numerical examples are considered and solved by using different variant of quantum-behaved particle swarm optimization algorithms. [ABSTRACT FROM AUTHOR]

Optimizing aircraft maintenance, repair and overhaul (MRO) planning, i.e., reviewing aircraft history based on when and where the aircraft should be serviced, is essential to ensuring aviation safety and operations. This paper examines a method for optimizing the scheduling of MRO operations for complex products. It has been shown in the literature that scheduling optimization of MRO operations is an NP-hard problem. In this study, the planning problem for MRO service resources is first analyzed, a mathematical model for the MRO process planning problem is established, and a multi-objective mathematical model for minimizing makespan time, maintenance time, and cost is proposed to represent the planning problem. Then, to optimize the makespan time, maintenance time, and cost, we first generate the maintenance, repair and overhaul transaction path (MROTP) data to utilize the PLM system efficiently, which ensures the effective operation of MRO actions throughout the aircraft lifecycle. MROTP data are obtained with engineering studies in our proposed research, and received data are retained in the PLM system. Finally, the modified ACO algorithm that performs scheduling optimization of the MRO process and the integration process of PLM with MRO operations is presented. The proposed algorithm is tested and validated by simulations in MATLAB software with the data obtained from the leading MRO company in Turkey. The Gurobi Solver is used to obtain the optimal solution by converting the problem into mixed-integer linear programming (MILP). The experimental setup is run using Gurobi with a 1-hour time limit, and the best solution is considered with an average run time calculated in seconds. When solving complicated instances, the MILP model-based approach is challenging. The study and comparison of the findings demonstrate that the proposed ACO-based algorithms have an advantage in planning MRO operations. The proposed ACO algorithm performs better than the other metaheuristic algorithms and the MILP model regarding maintenance time and cost. [ABSTRACT FROM AUTHOR]

Segmentation refers to the process of dividing an image into multiple regions based on some criteria such as intensity and color. In recent years, color image segmentation has received considerable attention from the researchers. However, it is still a highly complicated task due to the presence of more attributes or components as compared to monochrome images. Numerous meta-heuristics algorithms are developed to determine the optimal threshold value for segmenting color images efficiently. This paper presents an enhanced sine cosine algorithm (ESCA) to seek threshold for segmenting color images. Sine cosine algorithm (SCA) is a population-based optimization algorithm which has the ability of preventing local minima problem. First an input image is transformed to CIE L*a*b* color reduced space. ESCA is applied to determine the optimal threshold values for segmentation. The performance of the proposed method is tested on color images from Berkeley database, and segmentation results are compared with two metaheuristic algorithms, namely particle swarm optimization (PSO) and standard SCA. Experimental results are validated by measuring peak signal–noise ratio (PSNR), structural similarity index and computation time for all the images investigated. Results revealed that the proposed method outperforms the other methods like PSO and SCA by achieving PSNR of 23 dB and SSIM of 0.93 and also require less time for finding optimal threshold values than PSO and SCA. [ABSTRACT FROM AUTHOR]

The knapsack problem is one of the combinational optimization issues. This problem is an NP-hard problem. Soft computing methods, including the use of metaheuristic algorithms, are one way to deal with these types of problems. The standard Giza Pyramids Construction (GPC) algorithm is the first ancient-inspired algorithm that is published recently. In this paper, a binary version of the GPC algorithm for solving the 0-1 knapsack problem is proposed. For this purpose, this study uses both accumulative and multiplicative penalty functions as the objective function to determine infeasible solutions. To compare the performance, thirty different datasets have been created and the proposed algorithm has been compared with four popular and state-of-the-art algorithms. Statistical analysis has been used to find a significant difference in the performance of algorithms. The results and statistical analysis show that the proposed algorithm performs better than other metaheuristic algorithms. [ABSTRACT FROM AUTHOR]