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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]

Network function virtualization (NFV) is a contemporary network architecture concept that integrates complex network functions into software-based virtual network functions (VNFs) instead of specialized hardware. Resource allocation for multiple service function chain requests (SFC) in NFV-enabled networks is a fundamental challenge for network providers. Several research studies have focused on this problem in detail. Specifically, SFC routing attempts to satisfy each request by assigning a path across data centers that places the ordered set of required VNFs. This paper formulates this problem as an SFC provisioning problem (SFCRP) with the two objectives of maximizing the number of accepted requests and balancing the utilization of network resources. To address this problem, a two-stage routing algorithm (2PRT) that integrates Yen's algorithm and a genetic algorithm is proposed. The simulation results based on 12 scenarios that use three real-world networks and randomly generated requests indicate that the batch mode significantly enhances the acceptance rate compared to the existing methods. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is among the best evolutionary algorithms for global optimization. However, the basic DE has several shortcomings, like the slow convergence speed, and it is more likely to be stuck at local optima. Additionally, DE's performance is sensitive to its mutation strategies and control parameters for mutation and crossover. In this scope, we present in this paper three mechanisms to overcome DE limitations. First, two novel mutations called DE/mean-current/2 and DE/best-mean-current/2 are proposed and integrated in the DE algorithm, and they have both exploration ability and exploitation trend. On the other hand, to avoid being trapped in local minima of hard functions, a new exploration operator has been proposed called Weibull flight based on the Weibull distribution. Finally, new adapted control parameters based on the Weibull distribution are integrated. These parameters contribute to the optimization process by adjusting mutation scale and alleviating the parameter setting problem often encountered in various metaheuristics. The efficacy of the proposed algorithms called meanDE, MDEW, AMDE, and AMDEW is validated through intensive experimentations using classical tests, some challenging tests, the CEC2017, CEC2020, the most recent CEC2022, four constraint engineering problems, and the data clustering problem. Moreover, comparisons with several popular, recent, and high-performance optimization algorithms show a high effectiveness of the proposed algorithms in locating the optimal or near-optimal solutions with higher efficiency. The experiments clearly indicate the effectiveness of the new mutations compared to the standard DE mutations. Moreover, the proposed Weibull flight has a great capacity to deal with the hard composition functions of CEC benchmarks. Finally, the use of adapted control parameters for the mutation scale helps overcome the parameter setting problem commonly encountered in various metaheuristics. [ABSTRACT FROM AUTHOR]

Given the ever-growing electricity consumption and environmental anxiety with the predominant usage of conventional fuels in power plants, it is crucial to explore suitable alternatives to address these issues. Renewable energy sources (RESs) have emerged as the preferred choice for meeting energy requirements due to their minimal pollution. This study proposes a new idea to minimize operational costs and achieve the most cost-effective grid with minimum cost. Meanwhile, the transportation sector is gradually replacing conventional fossil-cars with electric ones, specifically plug-in electric vehicles (PEVs) and plug-in hybrid electric vehicles (PHEVs), which have gained significant consideration. These vehicles can join to the main grid and engage in energy exchange through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies. Additionally, the concept of microgrid (MG) is proposed to optimize the potential of PEVs through smart infrastructure. Using the V2G capability, the operating costs are reduced, providing opportunities to incorporate PEVs into the network. Therefore, effective operation of MGs becomes highly significant. This paper suggests management of a MG consisting of PEVs and RESs. The approach utilizes a stochastic programming technique called unscented transformation (UT). The problem is addressed as a single-objective stochastic optimization problem with the aim of minimizing the operation cost. The proposed approach employs the hybrid whale optimization algorithm and pattern search (HWOA–PS) to solve the stochastic problem. The obtained outcomes are compared with those of other approaches to validate its effectiveness. [ABSTRACT FROM AUTHOR]

This paper focuses on optimizing the laser cladding process parameters of WC/Inconel 625 composite on a low-carbon steel substrate. This process aims to improve industrial parts' wear and corrosion properties in corrosive and high-temperature environments. Laser power, laser cladding speed, powder-feeding rate, and WC content percentage were process input parameters, each at three levels. The dilution ratio and micro-hardness of samples were considered as process responses. Taguchi orthogonal arrays were employed for experimental design, and the results were analyzed using signal-to-noise ratio and analysis of variance. A linear regression model was developed to understand the relationship between process parameters and responses. In addition, two-objective optimization of the process parameters using the second-generation non-dominated sorting genetic algorithm (NSGA-II), and a hybrid algorithm based on the multi-objective whale optimization algorithm (MOWOAGA) were carried out to determine the optimal parameters that minimize the dilution ratio and maximize the cladding hardness. The results showed that the laser power and the powder-feeding rate have the most excellent effect on the dilution ratio. In contrast, WC content percentage and powder-feeding rate strongly affect micro-hardness. Based on the applied meta-heuristic algorithms and confirmed by experimental tests, the laser power of 370 W, laser cladding speed of 4 mm/s, powder-feeding rate of 8 g/min, and WC content percentage of 20.5% are the most optimal process parameters that lead to micro-hardness of 397 Vickers and dilution percentage of 2.5%. This research guides selecting, controlling, and optimizing WC/Inconel 625 composite laser cladding process parameters. [ABSTRACT FROM AUTHOR]

This paper proposes a jellyfish search algorithm (JSA)-based offline method to design a DC voltage controller of AC/DC front-end converter (FEC) in a doubly fed induction generator (DFIG)-based wind energy conversion system (WECS). First, a multi-objective tuning problem is formulated using a full-order converter model to achieve maximum utilization of converter parameters in controller design. Next, a normalized mono-objective problem is developed using a utopia-tracking approach to reduce the computational complexity in design. Finally, the mono-objective problem is solved using JSA to enhance the controller performance by obtaining optimal gain values for the controller. A comparative study is performed with other metaheuristic optimization techniques to justify the use of JSA in this application. The results confirm that the accuracy of the JSA-based approach is higher, whereas the execution time of the method is lower than the other methods. Furthermore, to validate the performance of the proposed approach, a DFIG-based WECS is modeled and simulated using OPAL-RT real-time simulator. A grid voltage-oriented vector control is used as the overall control algorithm for the FEC. Unlike the conventional pole-zero cancellation method, the proposed approach provides no voltage overshoot during voltage development. Compared to the published literature, it also ensures 40.65% lesser voltage deviation without any overcurrent, irrespective of wind speed and load variations. [ABSTRACT FROM AUTHOR]

Due to the environment's instability, to reduce inconsistent power supply it is preferable to connect two or more RES in a grid. Harmonics and other power quality issues are introduced into the distribution grid as a result of integrating these Hybrid Renewable Energy Storage (HRES) with nonlinear loads, which is a significant concern for the utility and the consumers, respectively. With the use of the Unified Power Quality Conditioner (UPQC), power quality problems such as voltage interruptions, actual power, and reactive power can be reduced. For optimal operation of the series and shunt compensator of UPQC, the outputs of the converters are controlled by PWM signals, which are optimally tuned by controllers implemented with updated algorithms. An optimized Hybrid metaheuristic-assisted collateral controller comprising of fractional-order proportional integral derivative (FOPID) controller cum (proportional integral (PI) controller for the enhancement of the Three-Phase HRES system-based Distribution Grid integrated with UPQC is developed in this paper. The UPQC reduces the harmonics that non-linear loads feed into the power supply and power quality concerns. The DC link voltage regulation and controller optimization are achieved with the optimal tuning of gain parameters, fractional orders and weight parameters using a hybrid Metaheuristic Algorithm named Amplified Slime Mould with WildeBeest Herd Optimization (ASM-WHO) Algorithm. The proposed system is developed in MATLAB Simulink to compare the effectiveness of compensation for voltage sags and surges and total harmonic distortion (THD) to more traditional approaches, UPQC with PI controller, FOPID-PI controller, FOPID-PI with Chicken Swarm Optimization (CSO), SOA, SMA, and WHO optimization. The settling time for the proposed FOPID-PI controller with ASM –WHO is 147.27 s, which is 1.7%, 13.78%, 1.338% and 13.66% better than the controller without optimization, the proposed collateral controller method with either SOA or CSO or SMA, WHO optimization and PI controller respectively. [ABSTRACT FROM AUTHOR]

This paper developed a new and efficient approach, the metaheuristic optimisation approach based on the Kalman filter (KF), to accurately identify the infinite impulse response (IIR) system coefficients. A recently developed metaheuristic optimisation technique called the manta-ray foraging optimisation (MRFO) algorithm has been employed to solve the proper parameters tuning exertion of KF. The problem of achieving better unknown state estimations by the conventional KF is closely connected with decent prior information about the noise statistics (measurement and process noise). However, the statistics mentioned above depend on the types of applications and process dynamics. The use of insufficiently known priori filter statistics would reduce the precision of the estimated filter or sometimes lead to a practical divergence of the filter. To alleviate this problem, the proposed technique initially produces optimally tuned KF parameters using the MRFO algorithm, and then the conventional KF method estimates the coefficients of the IIR model using the optimised KF parameters obtained at the beginning. The simulation studies have been carried out on three distinct order benchmark IIR systems (both same and reduced-order), and two prominent IIR applications called direct current (DC) motor and fractional order digital differentiator (FODD) using the proposed method. The overall identification results demonstrate that the proposed MRFO-KF technique significantly improves performance metrics over competing techniques, such as basic KF, other employed metaheuristic-based KF schemes and recently reported state-of-the-art algorithms. [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]

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]

This paper introduces a planning mechanism for optimal feeder reconfiguration, DSTATCOM allocation, and sizing in unbalanced radial distribution systems. The minimization of total active power losses (TACPL) is considered as the main objective of this work. A three phase forward–backward sweep load flow algorithm is utilized as a subroutine for the computation of the TACPL. The seagull optimization algorithm (SOA) is used as the solution methodology for this mixed integer non-linear planning problem. The simultaneous feeder reconfiguration, DSTATCOM installation obtains significant reduction in in the 19- and 25- bus unbalanced distribution network. Furthermore, the bus voltage magnitudes (pu) is also found to be enhanced. The SOA obtains lower TACPL in comparison to some of the well-known optimization algorithms like, differential evolution algorithm (DEA), moth-flame optimization (MFO) algorithm, and Jaya optimization algorithm (JOA). The proposed technique also attains lower TACPL in comparison to some of the established techniques reported in the literature while determining the optimal location, and rating of the DSTATCOM considering TACPL minimization. [ABSTRACT FROM AUTHOR]

Multiple unmanned aerial vehicle (UAV) path planning is the benchmark problem of multiple UAV application, which belongs to the non-deterministic polynomial problem. Its objective is to require multiple UAV to fly safely to the goal position according to their specific start position in three-dimensional space. This issue can be defined as a high-dimensional optimization problem, the solution of which requires optimization techniques with global optimization capabilities. Equilibrium optimizer (EO) is a population-based meta-heuristic algorithm. To improve the optimization ability of EO to solve high-dimensional problems, this paper proposes a modified equilibrium optimizer with generalized opposition-based learning (MGOEO), which improves the population activity by increasing the internal mutation and cross of the population. In addition, the generalized opposition-based learning is used to construct the population, which can effectively ensure that the algorithm has ability to jump out of the limitation of local optimal. First, numerical experiments show that MGOEO has better optimization precision than EO and several other swarm intelligent algorithms. Then, the paths of UAVs are simulated in three different obstacle environments. The simulation results show that MGOEO can obtain safe and smooth paths, which are better than EO and other eight state-of-the-art optimization algorithms. [ABSTRACT FROM AUTHOR]

Human Learning Optimization (HLO) is an emergent inborn binary meta-heuristic inspired by human learning mechanisms. To solve continuous problems efficiently, continuous HLO (CHLO) variants were presented. However, the research on CHLO is at its very initial stage and we find that the underlying cooperation between the learning operators in previous CHLO variants is not ideal, which degrades the overall search ability. To tackle this drawback, this paper develops a new social learning operator (SLO) and presents a novel CHLO with Enhanced Exploration and Exploitation (CHLOEEE), in which the new SLO can efficiently explore a wider area and consequently strengthens the exploration ability. Thanks to the new SLO, the individual learning operator in CHLOEEE is totally relieved from the exploration duty and focuses on local search to further enhance the exploitation capability. By comparing and analyzing the search behaviors of CHLO variants, the advantages of CHLOEEE are clearly explained and the insight of the inherent mechanisms is revealed. Finally, the proposed CHLOEEE is evaluated on the CEC15, CEC17, and engineering benchmark problems and compared with the previous CHLO variants and other nine state-of-the-art meta-heuristic algorithms. The results show that CHLOEEE is superior to the latest CHLO variant, named CHLOEE, on two-thirds of all the benchmark functions and significantly outperforms the other algorithms on at least 12 out of 15 CEC15 functions and 21 out of 30 CEC17 functions. Furthermore, CHLOEEE achieves the best results on all the 9 engineering optimization problems. [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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]