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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]

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]

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]

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]

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]

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]

The field of Metaheuristics has produced a large number of algorithms for continuous, black-box optimization. In contrast, there are few standard benchmark problem sets, limiting our ability to gain insight into the empirical performance of these algorithms. Clustering problems have been used many times in the literature to evaluate optimization algorithms. However, much of this work has occurred independently on different problem instances and the various experimental methodologies used have produced results which are frequently incomparable and provide little knowledge regarding the difficulty of the problems used, or any platform for comparing and evaluating the performance of algorithms. This paper discusses sum of squares clustering problems from the optimization viewpoint. Properties of the fitness landscape are analysed and it is proposed that these problems are highly suitable for algorithm benchmarking. A set of 27 problem instances (from 4-D to 40-D), based on three well-known datasets, is specified. Baseline experimental results are presented for the Covariance Matrix Adaptation-Evolution Strategy and several other standard algorithms. A web-repository has also been created for this problem set to facilitate future use for algorithm evaluation and comparison. [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]

Combined heat and power economic dispatch (CHPED) problem is one of the challenging issues in power system control, and operation, that searches for the optimal values of power and heat generation of different units to minimize the total operation cost. The interdependency of heat and power outputs of CHP units and valve point loading effects of power-only units make this problem very nonlinear and non-convex. So far, a wide variety of optimization algorithms based on simulation of various natural phenomena have been proposed and applied to solve this complex problem, claiming that the proposed algorithm is better than the similar optimization algorithms in terms of accuracy and run-time. In this paper, the CHPED problem is investigated in different case studies by using the imperialist competitive Harris hawks optimization (ICHHO) algorithm, as a combination of the imperialist competitive algorithm and Harris hawks optimization. The effectiveness of the proposed algorithm is first evaluated by testing it on the standard cases of 5, and 7 units as small-scale systems and 48-unit as the large-scale test system, considering the electrical losses. Also for the first time, the multi-zone CHPED (MZCHPED) problem is solved by a metaheuristic algorithm. The obtained results show that ICHHO algorithm reduces the operating costs in the range of 0.02–1.1%, compared to the best-reported results, in different single-zone CHPED (SZCHPED) problems. Also, the MZCHPED results, prove the effective performance of the algorithm compared to the similar SZCHPED problem. [ABSTRACT FROM AUTHOR]

A biomedical application of a novel metaheuristic optimizer is proposed in this paper by constructing an enhanced arithmetic optimization algorithm (AOA). The latter algorithm was constructed using the logarithmic spiral (Ls) search mechanism from the whale optimization algorithm and the greedy selection scheme from the differential evolution algorithm. The proposed algorithm (Ls-AOA) was tested against unimodal and multimodal benchmark functions and demonstrated better capability comparatively using other efficient metaheuristic algorithms reported in the literature. The constructed Ls-AOA algorithm was then proposed to design a proportional-integral-derivative (PID) controller employed in a functional electrical stimulation (FES) system for the first time. The initial statistical and convergence profile assessment showed better performance of the proposed algorithm. The comparative analyses for transient and frequency responses were performed for the PID-controlled FES system using the original AOA, sine–cosine and particle swarm optimization algorithms and the traditional Ziegler-Nichols tuning scheme. Similarly, the FES system tuned with the latter methods was also assessed for disturbance rejection and noise elimination. All the comparative analyses demonstrated that the proposed Ls-AOA has the greater capability for the challenging biomedical FES system. [ABSTRACT FROM AUTHOR]

Out of the various types of skin cancers, melanoma is observed to be the most malignant and fatal type. Early detection of melanoma increases the chances of survival which necessitates the need to develop an intelligent classifier that classifies the dermoscopic images accurately as melanoma or non-melanoma. Features extracted from an image have a major impact on the performance of a classifier. In this paper, handcrafted feature extraction techniques are used to extract features from dermoscopic images. It is quite possible that not all the features extracted from dermoscopic images contribute in the process of classification which imposes the need of selection of significant features from the feature set. Here, two binary variants of Harris Hawk Optimization (HHO) algorithm namely BHHO-S and BHHO-V are presented that employ S-shaped and V-shaped transfer functions with time-dependent behavior, respectively, for feature selection. The selected features are given to a classifier that classifies the dermoscopic image as melanoma or non-melanoma. Comparison of the performance of the proposed methods is done with existing metaheuristic algorithms. The results obtained after experimentation show the superiority of classifier that uses features selected using BHHO-S over BHHO-V and the classifiers that use existing state-of-the-art metaheuristic algorithms. The experimental results also reveal that texture features extracted using local binary pattern along with color features provides higher classification accuracy as compared to global and other local texture feature extraction techniques. [ABSTRACT FROM AUTHOR]

In the recent years, the optimization problems show that they are a big challenge for engineering regarding the fast growth of new nature-inspired optimization algorithms. Therefore, this paper presents a novel nature-inspired meta-heuristic algorithm for optimization which is called as bear smell search algorithm (BSSA) that takes into account the powerful global and local search operators. The proposed algorithm imitates both dynamic behaviors of bear based on sense of smell mechanism and the way bear moves in the search of food in thousand miles farther. Among all animals, bears have inconceivable sense of smell due to their huge olfactory bulbs that manage the sense of different odors. Since the olfactory bulb is a neural model of the vertebrate forebrain, it can make a strong exploration and exploitation for optimization. According to the odors value, bear moves the next location. Therefore, this paper mathematically models these structures. To demonstrate and evaluate the BSSA ability, numerous types of benchmark functions and four engineering problems are employed to compare the obtained results of BSSA with other available optimization methods with several analyzed indices such as pair-wise test, Wilcoxon rank and statistical analysis. The numerical results revealed that proposed BSSA presents competitive and greater results compared to other optimization algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates a meta-heuristic (MH) for the Pareto-optimal dissimilar path problem (DPP) (PDPP) whose solution is a set composed of at least two different paths. The objective vector of a PDPP includes some conflicting objectives: on the one hand, the average path measures such as the length and risk of paths in a solution must be kept low and, on the other hand, the dissimilarity among these paths should be kept high. The dissimilarity of the DPP is a measure of a paths set with cardinality no less than two. However, just one path can be extracted from a chromosome in the existing MHs for various path problems. This results in a great difficulty to evaluate the chromosome in the existing MHs when we apply them to solve DPP and, consequently, there exists no MH for solving the DPP so far. In this paper, a new decoding approach of a chromosome is first explored and, with this approach, a set of paths can be extracted from a chromosome. By combining the simulated annealing (SA), in which the new decoding approach is adopted, with the well-known greedy randomized adaptive search procedure (GRASP), a SA-based GRASP for the PDPP is proposed. The proposed algorithm is compared against a most recent heuristic, whose performance is better than all of the early approaches, for the PDPP and the experimental results show that the proposed algorithm is able to quickly create superior approximation of the efficient set of the PDPP than the existing solution approaches for the PDPP. [ABSTRACT FROM AUTHOR]

Several population-based techniques have subsequently been proposed. Despite their broad use in a variety of applications, we are still investigating the use of proposed methods to tackle real-world challenges. As a result, researchers must considerably modify and enhance their approaches based on the primary evolutionary processes in order to achieve rapid convergence, consistent equilibrium with high-quality data, and optimization. The paper proposes a fusion method (AOA–GOA) meta-heuristic optimization methods for global optimization tasks. They can be applied to different problems, including image processing, machine learning, wireless networks, power systems, engineering design, etc. The method fusion proposed is analyzed in context with GOA and AOA. To evaluate the performance, each method is tested on the same parameters like population size and number of iteration. The proposed IAOA is evaluated by varying the dimensions. The impact of varying dimensions is a standard test used in previous studies for optimizing test functions that show the effect of varying dimensions on efficiency of IAOA. From this, it is noted that it works efficiently for both high and low dimensional problems. In high dimensional problem, the proposed method gives efficient search results. [ABSTRACT FROM AUTHOR]

In the present paper, a physics-inspired metaheuristic algorithm is presented to solve multi-objective optimization problems. The algorithm is developed based on the concept of Newtonian cooling law that recently has been employed by the thermal exchange optimization (TEO) algorithm to solve single-objective optimization problems efficiently. The performance of the multi-objective version of TEO (MOTEO) is examined through bi- and tri-objective mathematical and engineering problems as well as bi-objective structural design examples. According to the comparisons between the MOTEO and several well-known algorithms, the proposed algorithm can provide quality Pareto fronts with appropriate accuracy, uniformity, and coverage for multi-objective problems. [ABSTRACT FROM AUTHOR]

Over the last decade, the size and complexity of real-world problems have grown dramatically, necessitating more effective tools. Nature-inspired metaheuristic algorithms have proven to be a promising tool for solving such problems due to their performance in a variety of fields. JAYA algorithm is a novel population-based algorithm which could have been able to present reliable results. This is because it does not need any parameters to be set other than the population size and the maximum number of iteration. Despite its positive feedbacks, this algorithm should be modified to witness more efficiency. This paper aims to amend the original version of Jaya to present a high-efficiency version named Powerful Enhanced Jaya (PEJAYA). In other words, the methodology of updating position in Jaya is modified to enhance the convergence and search capabilities. This approach is assessed according to solve 20 well-known benchmark functions, feature selection, and statistical tests. The output results of proposed optimization algorithm are then evaluated by comparing it with other recent algorithms including crow search algorithm (CSA), standard version of JAYA, particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), moth-flame optimization (MFO) and sine–cosine algorithm (SCA). Solving a real-world problem is another way of checking the efficiency of this approach with other published works. Prompt escape from local minima, superior convergence, and stability demonstrate that the suggested approach is a very powerful instrument that may be employed in a variety of optimization situations. [ABSTRACT FROM AUTHOR]

Model-based testing is an automated process in which executable tests are derived from behavioral models of a system. Model checking is a verification technique to reveal errors in which all reachable states of a system can be generated as state space. In the literature, different approaches suggest using model checkers for model-based testing. Model checker explores all possible system states, so utilizing the various paths in the state-space as test cases seems a promising solution. However, these approaches suffer from two main challenges. The first challenge is state space explosion, which prevents generating all reachable states by the model checker. The second one is generating redundant test cases. Recently, several methods using meta-heuristic and evolutionary approaches have been proposed to cope with these problems. Therefore, exploring a portion of state space using an optimization approach to detect the test objectives can be a proper way to manage the state space explosion and generate an optimal test suite with the least redundancy. In this paper, a method is proposed using a Bayesian optimization algorithm (BOA), and a model checker is as a bed to generate test cases for the service-oriented systems. In the proposed approach, the test suite is a set of paths on the state space starting from an initial state and leading to the state in which all the test objectives are satisfied. In this research, we have implemented BOA with three different structures in GROOVE toolset, an open-source toolset for designing and model checking graph transformation. Experimental results show that our solution generates better results in terms of coverage and speed in different case studies than the existing approaches. [ABSTRACT FROM AUTHOR]

The efficiency of handball goalkeepers is a good predictor of team ranking in tournaments, but despite this, very few studies have been carried out into the performance characteristics of elite goalkeepers. This paper provides the criteria for evaluating a handball goalkeeper and applies a variety of soft-computing methodologies for estimating their weights. More specifically, a fuzzy multi-criteria decision-making method, a metaheuristic optimisation algorithm, and statistical and domain-knowledge-based methods were used to evaluate the actions of goalkeepers during the game. Computer experiments were performed for all the proposed methodologies, using data from the 2020 European Men's Handball Championship, in order to estimate the weights of the indicators. Then, these weights were used to identify the best goalkeeper and identify and rank the top five goalkeepers as determined by the tournament organisers. The results obtained show that using the metaheuristic-based method is extremely helpful in quantifying the expert assessments, which are often challenging to express in a disaggregated form. The other two techniques offer a less optimal but more easily interpretable result for coaches and fans. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimization algorithm is proposed by hybridizing firefly and whale optimization algorithms and it is applied to the problem of determining optimal design parameters of model predictive control. The developed hybrid algorithm is compared with firefly and whale optimization algorithms considering unimodal, multimodal, composite and CEC-C06 2019 benchmark optimization problems in terms of statistical analysis. Comparison results showed that hybrid algorithm has the best performance for approximately 67% of 39 benchmark functions in finding the minimum in an average of independent 100 experiments for each function. For the functions in which the proposed algorithm gives the best results, approximately 64% improvement is achieved compared to FA and 39% improvement compared to WOA. In terms of convergence speed, in average 2% of improvement is obtained. As an application to engineering problems, optimization of parameters of the model predictive control for inverted pendulum and distillation column is considered and the performance of the proposed algorithm is compared with the performances of firefly algorithm and whale optimization algorithm. For the considered applications, a novel objective function is selected. The selected objective function for hybrid firefly–whale optimization algorithm enables finding the optimal prediction horizon and control horizon as well as the Q and R matrices such that the system responses satisfy desired settling time and maximum overshoot criteria. Most of considered comparisons demonstrate that the proposed hybrid algorithm has better performance. [ABSTRACT FROM AUTHOR]

Class imbalance problems have attracted the research community, but a few works have focused on feature selection with imbalanced datasets. To handle class imbalance problems, we developed a novel fitness function for feature selection using the chaotic salp swarm optimization algorithm, an efficient meta-heuristic optimization algorithm that has been successfully used in a wide range of optimization problems. This paper proposes an AdaBoost algorithm with chaotic salp swarm optimization. The most discriminating features are selected using salp swarm optimization, and AdaBoost classifiers are thereafter trained on the features selected. Experiments show the ability of the proposed technique to find the optimal features with performance maximization of AdaBoost. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to correct the unreasonable and inaccurate criticism to our previous experiments using Teaching-Learning-Based Optimization algorithm and to quantify the amount of error that may arise due to incorrect counting of fitness evaluations. It is shown that inexact experiment replication should be avoided in comparisons between meta-heuristic algorithms whenever possible. Otherwise, an inexact replication and margin of error should be explicitly reported. [ABSTRACT FROM AUTHOR]

Nowadays, use of various types of hybrid metaheuristic algorithms attracts the researchers to optimize the average profit or cost of an inventory system to avoid the local optimality due to high nonlinearity of the corresponding optimization problem. This paper deals with an application of binary tournament-based quantum-behaved particle swarm optimization algorithms on an imperfect production inventory problem with shortages. In order to reduce the production of defective items, modern/improvement technology has been incorporated in the production system. Also, the demand of the product is assumed to be dependent on its warranty period and selling price. The main objective of this study is to optimize the production rate, production period, selling price of the product, manufacturer's improvement technology level and maximum shortage level as well as maximize the average profit of the production system. For this purpose, three hybrid metaheuristic algorithms based on binary tournamenting and different variants of quantum-behaved PSO techniques have been developed. Then to examine the validity of the proposed model, three numerical examples have been solved. Considering each example, nonparametric statistical tests have been performed by using four different methods to analyze the performance of the used algorithms. Finally, sensitivity analyses have been performed to investigate the effects of different parameters on optimal policy. [ABSTRACT FROM AUTHOR]

Artificial neural network (ANN) is modeled to predict and classify problems. However, in the training phase of ANNs discovering faultless values of the weights of a network is extremely troublesome. Traditional weight updating methods often get stuck into local optima and converge to optimal solutions very slowly. Therefore, to overcome these drawbacks a modified version of a nature-based algorithm which merges meta-heuristics with weight-updating technique of ANN has been used in this paper. Whale optimization algorithm (WOA) is a well-established, efficient and competitive algorithm inspired by the hunting mechanism of the whales including their behavior in finding and attacking their prey with their bubble-net feeding technique. In WOA, the next location of the search individuals or whales is modified depending on some probability. Due to the high exploration rate of WOA, there is a disproportion between exploration and exploitation in the WOA and it also converges to the solution slowly. Thus, to establish an equilibrium between exploration and exploitation a new variant of WOA called modified whale optimization algorithm (MWOA) is proposed to overcome the problem of delayed convergence. In MWOA, roulette wheel selection is combined with WOA to enhance the convergence speed of WOA. MWOA is tested on 11 benchmark functions, and the outcomes are compared with WOA. The results prove that MWOA has gained success in overcoming the problem of the slow convergence of WOA. Also, the results show that the proposed MWOA technique, when applied to ANN, can overcome the problems of traditional techniques and has improved the results. [ABSTRACT FROM AUTHOR]

The gaining sharing knowledge based optimization algorithm (GSK) is recently developed metaheuristic algorithm, which is based on how humans acquire and share knowledge during their life-time. This paper investigates a modified version of the GSK algorithm to find the best feature subsets. Firstly, it represents a binary variant of GSK algorithm by employing a probability estimation operator (Bi-GSK) on the two main pillars of GSK algorithm. And then, the chaotic maps are used to enhance the performance of the proposed algorithm. Ten different types of chaotic maps are considered to adapt the parameters of the GSK algorithm that make a proper balance between exploration and exploitation and save the algorithm from premature convergence. To check the performance of proposed approaches of GSK algorithm, twenty-one benchmark datasets are taken from the UCI repository for feature selection. The performance is measured by calculating different type of measures, and several metaheuristic algorithms are adopted to compare the obtained results. The results indicate that Chebyshev chaotic map shows the best result among all chaotic maps which improve the performance accuracy and convergence rate of the original algorithm. Moreover, it outperforms the other metaheuristic algorithms in terms of efficiency, fitness value and the minimum number of selected features. [ABSTRACT FROM AUTHOR]

This paper proposes a novel metaheuristic-based optimization technique called search space reduction (SSR) optimization algorithm. This algorithm attempts to solve the common pitfalls in the existing algorithms in the literature by randomly generating the search agents in every iteration instead of following the best solution. This new algorithm is simple, computationally efficient, which is based on the concept of reducing the search space. The performance of this algorithm is tested over classical test functions and CEC'17 benchmark test functions. The results are compared with well-established algorithms in the literature. The test results show that the proposed algorithm exhibits good exploration and exploitation capabilities. Further, this algorithm also outperforms other algorithms in solving multimodal optimization problems. In addition to this, the computational complexity of this algorithm is also presented according to CEC'17 guidelines. The proposed algorithm is also employed to solve three engineering design problems and a more complex re-entry trajectory optimization problem to show its effectiveness. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have shown promising results in solving various optimization problems. The crow search algorithm (CSA) is a new and effective meta-heuristic algorithm that emulates crows' intelligent group behavior in nature. However, it suffers from several problems, such as trapping into local optimum and premature convergence. This paper proposes an improved crow search algorithm (ICSA), which has been tested and evaluated by a set of well-known benchmark functions. A new update mechanism that uses the merits of the global best position to move toward the best position is proposed. This mechanism increases the convergence of the algorithm and improves its local search-ability. Twenty benchmark functions are used to evaluate the performance of the proposed ICSA. Moreover, the ICSA algorithm is compared with the conventional CSA and other meta-heuristic algorithms such as particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), gray wolf optimizer (GWO), moth-flame optimization (MFO), and sine-cosine algorithm (SCA). The experimental result shows that the proposed ICSA algorithm has produced promising results and outperformed conventional CSA and other meta-heuristic algorithms. Also, the proposed ICSA has a more robust convergence for optimizing objective functions in terms of solution accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Novel metaheuristic algorithms are now considered an appealing collection of methods for solving complex optimization problems, in which the challenging objective is to find a better solution in a shorter computation time. Focusing on the same objective, this paper proposes a novel metaheuristic optimization algorithm inspired by heat transfer relationships based on the second law of thermodynamics. Imitating the heat transfer behavior of solid objects, the proposed method is called the heat transfer relation-based optimization algorithm (HTOA). This behavior was modeled on a heat transfer function used to measure temperature differences between the selected solutions and the best solution. This function was employed to determine and add the heat capacity transferred between those solutions. Finally, all the solutions were heat-exchanged with the best solution to select the fittest solution and exclude the rest. This procedure continued until the best solution or solutions were found. The proposed method is challenged by many famous benchmark problems in two categories as well as two real-world problems (PID controller and linear regression). The HTOA was then compared with a number of well-known and state-of-the-art optimization algorithms. Selecting better solutions and requiring shorter computation time, the proposed HTOA outperformed the other algorithms. [ABSTRACT FROM AUTHOR]

The classification accuracy of a multi-layer perceptron (MLP) depends on the selection of relevant features from the data set, its architecture, connection weights and the transfer functions. Generating an optimal value of all these parameters together is a complex task. Metaheuristic algorithms are popular choice among researchers to solve complex optimization problems. This paper presents a hybrid metaheuristic algorithm simple matching-grasshopper new cat swarm optimization algorithm (SM-GNCSOA) that optimizes all the four components simultaneously. SM-GNCSOA uses grasshopper optimization algorithm, a new variant of binary grasshopper optimization algorithm called simple matching-binary grasshopper optimization algorithm and a new variant of cat swarm optimization algorithm called new cat swarm optimization algorithm to generate an optimal MLP. Features play a vital role in determining the classification accuracy of a classifier. Here, we propose a new feature penalty function and use it in SM-GNCSOA to prevent underfitting or overfitting due to the selected number of features. To evaluate the performance of SM-GNCSOA, different variants of SM-GNCSOA are proposed and their classification accuracies are compared with SM-GNCSOA on ten classification data sets. The results show that SM-GNCSOA gives better results on most of the data sets due to its capability to balance exploration and exploitation and to avoid local minima. [ABSTRACT FROM AUTHOR]

In this paper we propose a Coral Reefs Optimization algorithm with substrate layers (CRO-SL) to tackle the battery scheduling optimization problem in micro-grids (MGs). Specifically, we consider a MG that includes renewable generation and different loads, defined by their power profiles, and is equipped with an energy storage device (battery) to address its scheduling (charge/discharge duration and occurrence) in a real scenario of variable electricity prices. The CRO-SL is a recently proposed meta-heuristic which promotes co-evolution of different exploration models within a unique population. We fully describe the proposed CRO-SL algorithm, including its initialization and the different operators implemented in the algorithm. Experiments in a real MG scenario are carried out. To show the good battery scheduling performance of the proposed CRO-SL, we have compared the results with what we called a deterministic procedure. The deterministic charge/discharge approach is defined as a fixed way of using the energy storage device that only depends on the pattern of the loads and generation profiles considered. Hourly values of both generation and consumption profiles have been considered, and the good performance of the proposed CRO-SL is shown for four different weeks of the year (one per season), where the effect of the battery scheduling optimization obtains savings up 10 % of the total electricity cost in the MG, when compared with the deterministic procedure. [ABSTRACT FROM AUTHOR]

In the last 3 decades, metaheuristic algorithms have received more popularity because of their superior performance to solve large and complex optimization problems. Most of these algorithms are inspired by biological phenomena, social behavior of animals, science and art. Among these four sources, the last one is utilized only by one algorithm. In this paper, we propose another novel art-inspired population-based metaheuristic, called color harmony algorithm (CHA), for solving the global optimization problems. The proposed method models its search behavior through combining harmonic colors based on their relative positions around the hue circle in the Munsell color system and harmonic templates. We utilize simultaneously four different fitness information to construct the hue groups, which improve search ability of the algorithm. CHA has two different phases including the concentration phase and the dispersion phase which are employed to explore and exploit the search space. The performance of the proposed method has been examined using several benchmark test functions commonly used in the literature. To show the effectiveness and robustness of the proposed method, the results are compared with those obtained using ten well-known metaheuristic algorithms. Also, the Wilcoxon Signed-Rank test is conducted to measure the pair-wise statistical performances of the algorithms. The results indicate that besides the simplicity of the proposed algorithm, CHA can outperform the other considered algorithms in terms of the convergence speed and the number of function evaluations. [ABSTRACT FROM AUTHOR]

This paper presents a novel metaheuristic algorithm named as life choice-based optimizer (LCBO) developed on the typical decision-making ability of humans to attain their goals while learning from fellow members. LCBO is investigated on 29 popular benchmark functions which included six CEC-2005 functions, and its performance has been benchmarked against seven optimization techniques including recent ones. Further, different abilities of LCBO optimization algorithm such as exploitation, exploration and local minima avoidance were also investigated and have been reported. In addition to this, scalability is tested for several benchmark functions where dimensions have been varied till 200. Furthermore, two engineering optimization benchmark problems, namely pressure vessel design and cantilever beam design, were also optimized using LCBO and the results have been compared with recently reported other algorithms. The obtained comparative results in all the above-mentioned experimentations revealed the clear superiority of LCBO over the other considered metaheuristic optimization algorithms. Therefore, based on the presented investigations, it is concluded that LCBO is a potential optimizer for engineering problems. [ABSTRACT FROM AUTHOR]

In this paper, a new optimization algorithm called the Hitchcock bird-inspired algorithm (HBIA) is proposed. It is inspired by the aggressive bird behavior portrayed by Alfred Hitchcock in the 1963 thriller "The Birds." It is noteworthy to emphasize that the bird's behavior as shown in the movie is itself inspired by a considered natural birds behavior when faced with extreme conditions. HBIA is a stochastic swarm intelligence algorithm that captures the essence of the fictional behavior of the phenomenon of birds throughout the Hitchcock's film and model an optimization mechanism. The algorithm is based on the attack pattern of birds in the film, which has the stages of lurking, attack and reorganization, defined by the initialization, movement strategies in the search space and strategy of local minimum escape, respectively. The technique has as differential the use of adaptive parameters, a discretized random initialization and the use of the beta distribution. In contrast to the existing ones, the proposed technique provides an efficient optimization in high-dimensionality cost functions, using adaptive parameters, a discretized random initialization and the use of the beta distribution. Its performance is analyzed and compared to classic techniques, such as PSO, ABC and CS, as well as to the existing adaptive techniques, such as sine cosine algorithm, whale optimization algorithm, teaching–learning-based optimization and vortex search. HBIA's performance is investigated by several experiments implemented through eight cost functions. The results show that the HBIA can find more satisfactory solutions in large search spaces and high dimensionality of the evaluated cost functions when compared to the existing optimization methods. [ABSTRACT FROM AUTHOR]

The simplicity and effectiveness of a recently proposed metaheuristic, butterfly optimization algorithm (BOA) have gained huge popularity among research community and are being used to solve optimization problems in various disciplines. However, the algorithm is suffering from poor exploitation ability and has a tendency to show premature convergence to local optima. On the other hand, the mutualism phase of another popular metaheuristic symbiosis organisms search (SOS) is known for its exploitation capability. In this paper, a novel hybrid algorithm, namely m-MBOA is proposed to enhance the exploitation ability of BOA with the help of mutualism phase of SOS. To evaluate the effectiveness of m-MBOA, thirty-seven (37) classical benchmark functions are considered and the performance of m-MBOA is compared with the performance of ten (10) state-of-the-art algorithms. Statistical tools have been employed to observe the efficiency of the m-MBOA qualitatively, and obtained results confirm the superiority of the proposed algorithm compared to the state-of-the-art metaheuristic algorithms. Finally, four real-life optimization problem, namely gear train design problem, gas compressor design problem, cantilever beam design problem and three-bar truss design problem are solved with the help of the newly proposed algorithm, and the results are compared with the obtained results of different popular state-of-the-art optimization techniques and found that the proposed algorithm is more efficient than the compared algorithms. [ABSTRACT FROM AUTHOR]

Designing an ANN is a complex task as its performance is highly dependent on the network architecture as well as the training algorithm used to select proper synaptic weights and biases. Choosing an optimal design leads to greater accuracy when the ANN is used for classification. In this paper, we propose an approach multilayer perceptron-lion optimization algorithm (MLP-LOA) that uses lion optimization algorithm to find an optimum multilayer perceptron (MLP) architecture for a given classification problem. MLP-LOA uses back-propagation (BP) for training during the optimization process. MLP-LOA also optimizes learning rate and momentum as they have a significant role while training MLP using BP. LOA is a population-based metaheuristic algorithm inspired by the lifestyle of lions and their cooperative behavior. LOA, unlike other metaheuristics, uses different strategies to search for optimal solution, performs strong local search and helps to escape from worst solutions. A new fitness function is proposed to evaluate MLP based on its generalization ability as well as the network's complexity. This is done to avoid dense architectures as they increase chances of overfitting. The proposed approach is tested on different classification problems selected from University of California Irvine repository and compared with the existing state-of-the-art techniques in terms of accuracy achieved during testing phase. Experimental results show that MLP-LOA performs better as compared to the existing state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Plenty of problems are related to the calculation of edges and nodes in the realistic networks. It also influences the realization of shortest path problem (SPP) because of its essential fuzziness. This paper presents a fuzzy-based modified particle swarm optimization (fuzzy-based MPSO) algorithm for resolving the shortest path issue. The proposed work also evaluates the uncertainties of this shortest path problem through the utilization of offered algorithm. Actually, the normal PSO algorithm is altered and estimated to tackle the fuzzy-based SPP (FSPP) with uncertain edges. The performance of the planned algorithm will be improved; also the results are compared with the existing methodologies. The early convergence of the PSO technique can be alleviated and travelled via the dynamic operation of fuzzy method. And the proposed method is compared with other metaheuristic algorithms such as evolutionary random weight networks (GA-RWNs), grasshopper optimization algorithm with evolutionary population dynamics (GOA-EPD), levy weight grey wolf optimization (LGWO) and PSO in terms of cost and time consumption. The related results and discussion is performed in the working platform of MATLAB tool for the demonstration of the proposed work to manage the FSPP in indeterminate networks. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of fault diagnosis performed on a mechanical system, based on acquired vibrations from bearings. In this aim, an optimization algorithm resulted from the alliance between a time–frequency–scale signal processing method (the matching pursuit) and an evolutionary computing technique (mainly, a genetic algorithm) is introduced. The matching pursuit method itself leads to a NP-hard procedure, but, with the help of a metaheuristic, the procedure becomes computationally efficient. A generalization of Baker's procedure implementing the stochastic universal sampling mechanism, as well as a new concept, namely the Boltzmann annealing selection, is introduced, in order to design the genetic algorithm appropriately. This latter not only plays an important role in convergence speed, but also constitutes the basis of a (self) adaptive mechanism aiming to keep in balance the exploration and exploitation features. Based on the optimal solution found through the genetic matching pursuit procedure, the bearings fault diagnosis can successfully be performed, even in case of multiple defects and without prior training of some defect classification model. [ABSTRACT FROM AUTHOR]

In this paper, the water cycle algorithm (WCA), a recently developed metaheuristic method is proposed for solving multi-objective optimization problems (MOPs). The fundamental concept of the WCA is inspired by the observation of water cycle process, and movement of rivers and streams to the sea in the real world. Several benchmark functions have been used to evaluate the performance of the WCA optimizer for the MOPs. The obtained optimization results based on the considered test functions and comparisons with other well-known methods illustrate and clarify the robustness and efficiency of the WCA and its exploratory capability for solving the MOPs. [ABSTRACT FROM AUTHOR]

Clustering Search ( *CS) has been proposed as a generic way of combining search metaheuristics with clustering to detect promising search areas before applying local search procedures. The clustering process may keep representative solutions associated with different search subspaces. In this paper, new approaches are proposed, based on *CS, as an Artificial Bee Colony-based one, which detects promising food sources alike *CS approaches. The other new *CS approach is based on Differential Evolution (DE) algorithm. The DE is just a CS component (the evolutionary algorithm), different from ABC-based approach, called Artificial Bee Clustering Search (ABCS). ABCS tries to find promising solutions using some concepts from CS. The proposed hybrid algorithms, performing a Hooke and Jeeves-based local, are compared to another hybrid approaches, exploring an elitist criteria to apply local search. The experiments show that the proposed ABCS and Differential Evolutionary Clustering Search (DECS) are competitive for the majority continuous optimization functions benckmarks selected in this paper. [ABSTRACT FROM AUTHOR]

In this paper, we propose a multi-objective imperialistic competitive algorithm (MOICA) for solving global multi-objective optimization problems. The MOICA is a modified and improved multi-objective version of the single-objective imperialistic competitive algorithm previously proposed by Atashpaz-Gargari and Lucas (IEEE Congr Evolut Comput 7:4661-4666. doi:10.1109/CEC.2007.4425083, 2007). The presented algorithm utilizes the metaphor of imperialism to solve optimization problems. Accordingly, the individuals in a population are referred to as countries, of which there are two types—colonies and imperialists. The MOICA incorporates competition between empires and their colonies for the solution of multi-objective problems. To this end, it employs several non-dominated solution sets, whereby each set is referred to as a local non-dominated solution (LNDS) set. All imperialists in an empire are considered non-dominated solutions, whereas all colonies are considered dominated solutions. In addition to LNDS sets, there is one global non-dominated solution (GNDS) set, which is created from the LNDS sets of all empires. There are two primary operators in the proposed algorithm, i.e., assimilation and revolution, which use the GNDS and LNDS sets, respectively. The significance of this study lies in a notable feature of the proposed algorithm, which is that no special parameter is used for diversity preservation. This enables the algorithm to prevent extra computation to maintain the spread of solutions. Simulations and experimental results on multi-objective benchmark problems show that the MOICA is more efficient compared to a few existing major multi-objective optimization algorithms because it produces better results for several test problems. [ABSTRACT FROM AUTHOR]

Behavioral teaching procedures can be used to promote the individualized learning of reading skills for children, and computational processes can assist instructors in the generation of a set of tasks. However, the automatic generation of these tasks can be unfeasible due to the high-order search space for the possible combinations of tasks; this complexity increases when considering the possible constraints as well as adapting the tasks to the individual characteristics of each student. This paper presents a new method to automatically generate teaching matching-to-sample tasks, adapting the difficulty by using bio-inspired optimization metaheuristics. Genetic algorithms, ant colony optimization, and integer and categorical particle swarm optimization were evaluated to determine their stability and capacity to generate adapted tasks. A comparison of the results between the algorithms showed a better rate of convergence for the genetic algorithms, which were able to generate tasks at an adapted level of difficulty to students. These tasks were applied to a group of students at a Brazilian public school in the early stages of a literacy course indicating satisfactory effects in the individual learning process. [ABSTRACT FROM AUTHOR]

The selection mechanisms that are most commonly adopted by multi-objective evolutionary algorithms (MOEAs) are based on Pareto optimality. However, recent studies have provided theoretical and experimental evidence regarding the unsuitability of Pareto-based selection mechanisms when dealing with problems having four or more objectives. In this paper, we propose a novel MOEA designed for solving many-objective optimization problems. The selection mechanism of our approach is based on the transformation of a multi-objective optimization problem into a linear assignment problem, which is solved by the Kuhn-Munkres’ (Hungarian) algorithm. Our proposed approach is compared with respect to three state-of-the-art MOEAs, designed for solving many-objective optimization problems (i.e., problems having four or more objectives), adopting standard test problems and performance indicators taken from the specialized literature. Since one of our main aims was to analyze the scalability of our proposed approach, its validation was performed adopting test problems having from two to nine objective functions. Our preliminary experimental results indicate that our proposal is very competitive with respect to all the other MOEAs compared, obtaining the best results in several of the test problems adopted, but at a significantly lower computational cost. [ABSTRACT FROM AUTHOR]

Over the last decade, several metaheuristic algorithms have emerged to solve numerical function optimization problems. Since the performance of these algorithms presents a suboptimal behavior, a large number of studies have been carried out to find new and better algorithms. Therefore, this paper proposes a new metaheuristic algorithm, namely the car tracking optimization algorithm; it is inspired by observing the programming methods of other metaheuristic algorithms. And the proposed algorithm has been tested over 55 benchmark functions, and the results have been compared with firefly algorithm (FA), cuckoo searching algorithm (CS), and vortex search algorithm (VS). The results indicate that the performance of the proposed algorithm surpasses FA, CS, and VS algorithm. [ABSTRACT FROM AUTHOR]

Harmony search (HS) has shown promising performance in a wide range of real-world applications. However, in many cases, the basic HS exhibits strong exploration ability but weak exploitation capability. In order to enhance the exploitation capability of the basic HS, this paper presents an improved global harmony search with generalized opposition-based learning strategy (GOGHS). In GOGHS, the valuable information from the best harmony is utilized to enhance the exploitation capability. Moreover, the generalized opposition-based learning (GOBL) strategy is incorporated to increase the probability of finding the global optimum. The performance of GOGHS is evaluated on a set of benchmark test functions and is compared with several HS variants. The experimental results show that GOGHS can obtain competitive results. [ABSTRACT FROM AUTHOR]

Real parameter optimization is an important task in almost all engineering applications. This paper introduces a novel multiagent architecture and agent interaction mechanism for the solution of single objective type real-parameter optimization problems. The proposed multiagent system includes several metaheuristics as problem-solving agents that act on a common population containing the frontiers of search process and a common archive keeping the promising solutions extracted so far. Each session of the proposed architecture includes two phases: a tournament among all agents to determine the currently best performing agent and a search procedure conducted by the winner. In the tournament phase, each agent performs a fixed number of fitness evaluations over the common population and gets a success score in terms the fitness improvements it achieved by itself. The agent with the best score is the winner of the tournament. Then, the winner agent is allowed to conduct its search algorithm using the common population until its procedure gets stuck at a locally optimal solution or maximum fitness evaluations per session is reached. Afterwards, the procedure restarts with another tournament to determine the next winner. In all phases and iterations of the proposed framework, all agents use the same population and archive in conducting their search procedures. This way, agents compete with each other in terms of their fitness improvements achieved over a fixed number of fitness evaluations in tournaments, and they cooperate by sharing their search experiences through accumulating them in a common population and a common archive. The proposed multiagent system is experimentally evaluated using the well-known CEC2005 benchmark problems set. Analysis of the obtained results exhibited that the proposed framework performs significantly better than its state-of-the-art competitors in almost all problem instances. [ABSTRACT FROM AUTHOR]

In these recent years, several metaheuristic optimization algorithms have been developed to solve complex real-world problems. However, according to the no free lunch theorem, there is no metaheuristic algorithm which has the best suited for solving all optimization problems. Therefore, developing new metaheuristic algorithms is an open problem. Two major categories of metaheuristics are swarm intelligence and evolutionary algorithms. Both of them have their own advantages. In this paper, we present a naturally swarm and evolutionary algorithm in which its particles have memory, collaboration, information sharing, and competition inherited from one specific analogy. So, it has the advantages of both swarm and evolutionary algorithms without complications of hybridizing them. We formulate a new metaheuristic optimization algorithm called open source development model algorithm (ODMA) inspired by open source development model and communities, in such a way that each potential solution is considered as a software, and by evolution of the softwares, better solutions of the function that should be optimized are searched. The main operations of the algorithm include employing features and methods of leading softwares, evolution of leading softwares based on their history and forking from leading softwares. After a detailed formulation and explanation of its implementation, the algorithm is evaluated by $$17$$ well-known benchmark functions, and the results are compared with PSO, ICA, and GA. The results show that ODMA outperforms GA, PSO, and ICA in terms of finding the global optimum and convergence. [ABSTRACT FROM AUTHOR]

The minimum attribute reduction problem in the context of rough set theory is an NP-hard nonlinearly constrained combinatorial optimization problem. In this paper, we propose an efficient and competitive combinatorial artificial bee colony algorithm for solving the minimum attribute reduction problem. In the proposed algorithm, a new multidimensional binary local search scheme for bee colonies based on velocity computation is presented; an employed bee and its recruited onlooker bees use different local search strategies so as to get a possibly more diversified neighboring search around a current food source; the information of the so-far best solution is exploited in various ways by employed bees, onlookers and scouts, respectively; the monotonicity property of classification quality of attribute subsets from the theory of rough sets is employed to avoid possibly invalid local searches. Performance comparisons with some best performing population-based metaheuristic algorithms for the minimum attribute reduction problem were carried out on a number of UCI data sets. The experimental results show that the proposed algorithm overall outperforms all the other algorithms in terms of solution quality and is therefore promising for solving the minimum attribute reduction problem. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new parallel optimization algorithm that combines ideas from the fields of metaheuristics and Systolic Computing. The algorithm, called Systolic Genetic Search (SGS), is designed to explicitly exploit the high degree of parallelism available in modern Graphics Processing Unit (GPU) architectures. In SGS, solutions circulate synchronously through a grid of processing cells, which apply adapted evolutionary operators on their inputs to compute their outputs that are then ejected from the cells and continue moving through the grid. Four different variants of SGS are experimentally studied for solving two classical benchmarking problems and a real-world application. An extensive experimental analysis, which considered several instances for each problem, shows that three of the SGS variants designed are highly effective since they can obtain the optimal solution in almost every execution for the instances and problems studied, as well as they outperform a Random Search (sanity check) and two Genetic Algorithms. The parallel implementation on GPU of the proposed algorithm has achieved a high performance obtaining runtime reductions from the sequential implementation that, depending on the instance considered, can arrive to around a hundred times, and have also exhibited a good scalability behavior when solving highly dimensional problem instances. [ABSTRACT FROM AUTHOR]

In this study, the permutation flow shop scheduling problem with makespan criteria is investigated, and two algorithms, Jaya and SAMP-Jaya, are proposed. Jaya is a simple and effective meta-heuristic optimization technique that has been successfully applied in various engineering problems, while SAMP-Jaya is a modified version of Jaya that uses a multi-population approach. Both algorithms are parameter-less, and the ANOVA experiment is used to identify the significant control parameters. The population size is found to be the most crucial parameter for improving solution quality. The performance of Jaya and SAMP-Jaya is compared to the exact solution and the genetic algorithm, demonstrating the superiority of the proposed algorithms. The computational results confirm that the suggested optimization techniques are effective in terms of solution quality. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc Networks (VANETs) can be designed in a way to organize road protection with no specific need for any fixed infrastructure. Accordingly, the movement of all vehicles can be planned according to perceived information, and Quality of Services Routing (QoSR) algorithms can be pressured according to its available options, paths, and links, and according to the criteria and reliability of the QoSR. Ensure that QoSR is aware of the environment of the network of vehicles, including location of vehicles, direction, and speed. This study is to reduce the effects of unpredictable problems on the best pathway to replace the broken path/link. A QoSR with Particle Swarm Optimization (QoSR-PSO) is used in this article for improving QoSs in vehicular ad hoc networks. By modeling the behavior of a group of particles, particle swarm optimization algorithms find the best possible solution to the problem. In order to perform simulation experiments, NS2 simulator and VanetMobisim have been used. The comparison results with benchmark studies show the improvement in packet delivery rate (PDR), delay, Packet Drop, and overload. [ABSTRACT FROM AUTHOR]