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High-utility itemsets mining (HUIM) is an important research topic in data mining that consists of extracting efficient itemsets that generate high profit in databases. Metaheuristic algorithms are good candidates for finding some sufficiently good high-utility itemsets (HUIs) while reducing the prohibitive computational cost incurred by such mining. However, metaheuristic algorithms suffer from crucial issues: runtime and memory consumption, entailing scalability issues. To contribute in alleviating these drawbacks, we explore a hybrid approach combining metaheuristics with an optimization technique, and experimentally show the obtainment of better quality solutions. The exploration component in our approach relies on the crow search algorithm (CSA) which is a method that presents a strong capacity in the global search process. The exploitation component is based on the particle swarm optimization (PSO) which is an established stochastic optimization technique known to have a good convergence capability. This article proposes the HUIM-ICSO algorithm, coupling CSA and PSO, as a hybrid method for mining HUIs. In addition to the proposed main CSA-based algorithm, two additional novel enhanced variants are incorporated and integrated with PSO. Experiments are undertaken on 12 publicly available datasets and compared with state-of-the-art algorithms, such as mHUIMiner, HUIM-SPSO and HUIM-AF. Experiments show that, as the number of transactions or items grows, the suggested approach remains scalable and delivers favorable outcomes in terms of runtime, memory usage, and the quantity of HUIs mined, all while maintaining a good convergence rate. [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]

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]

Jaya is a metaheuristic algorithm that uses a pair of random internal parameters to adjust its exploration and exploitation search behaviors. Such a random setting can negatively affect the search performance of the algorithm by causing inappropriate search behavior in some iterations. To tackle this issue, the present study deals with developing a new fuzzy decision-making mechanism for dynamic adjusting the trade-off between the exploration and exploitation search behaviors of the Jaya method. The new algorithm is named Fuzzy Reinforced Jaya (FRJ) method. The search capability of the FRJ is evaluated in solving a suite of unconstrained mathematical benchmarks and constrained mechanical and structural optimization problems with buckling and natural frequency constraints. Also, the relevant decision variables are selected from both continuous and discrete domains. To provide a deeper insight into the effect of the defined auxiliary fuzzy module, the performance of the algorithm is evaluated and discussed using normalized diversity concept and behavioral diagrams. Also, employing different statistical analyses (e.g., Q–Q diagrams, Wilcoxson and Friedman tests), the significance of the outcomes is evaluated. Also, the numeric achievements are compared with six other well-stablished techniques. Attained outcomes indicate that the proposed FRJ, as a self-adaptive and parameter-free method, provides superior and promising results in the terms of stability, accuracy, and computational cost in solving mathematical and structural optimization problems. [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]

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]

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]

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]

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]

The kNN classifier is the most popular, supervised machine-learning technique, but the main disadvantage of this algorithm is that it has restricted access to the class distributions in a training point set and treats all the instances equally. In kNN classification, fuzzy sets are used to obtain the membership degrees of each point to the classes known as fuzzy kNN (FkNN) classification. Although the FkNN classifier enhances the performance of the kNN, it does not consider the effect of noisy and redundant instances, which makes it ineffective. Moreover, the performance of kNN is dependent on the value of k (number of nearest neighbours). Considering these issues, we present a novel algorithm that simultaneously tunes the class-dependent feature weights and k value using an effective meta-heuristic algorithm, the Enhanced Equilibrium Optimization technique. Several experiments have been conducted on different biomedical datasets, and the proposed approach has outperformed the other standard classifiers in terms of accuracy. [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]

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]

Image contrast enhancement (ICE) is an important step in image processing and analysis as the quality of an image plays a pivotal role in human understanding. Moreover, contrast is considered a key aspect for the assessment of picture quality. Incomplete beta function (IBF) is one of the widely used transformations and histogram equalization (HE) is also one of the most popular methods used for this task. However, HE has some limitations as the local contrast of an image cannot be uniformly enhanced. In the present work, a contrast enhancement method is proposed for grey-scale images based on a recent socio-inspired meta-heuristic called political optimizer (PO). The PO algorithm follows the multi-phased process of politics. The exploitative capability of PO is improved by combining it with the adaptive β -hill climbing (A β HC) which is regarded as one of the best local search techniques. The hybridization of these two algorithms is then used to find the optimal values of pixels which can intensify the hidden characteristic of the low-contrast images. The proposed algorithm is tested over a publicly available Kodak image dataset along with some standard images and evaluated in terms of standard metrics. The experimental results demonstrate that the proposed method can successfully outperform many existing methods considered here for comparison. [ABSTRACT FROM AUTHOR]

The present investigation includes the performance enhancement concept of discrete meta-heuristics (MHs) for truss design with concurrent size and topology optimization. The five basic MHs, viz. teaching–learning-based optimization (TLBO), whale optimization algorithm (WOA), dragonfly algorithm (DA), heat transfer search (HTS), and ant lion optimization (ALO) algorithm, are investigated. Often these MHs found incompetent in answering complex problems like concurrent topology and sizing optimization of truss structures due to their poor convergence rate, local optima trap, and higher computation time. Also, the balance between diversification and intensification is very significant for MHs efficiency. A mutation is a strong strategy for balancing the diversification and intensification of MHs and can assist in guiding the population towards the global optimum. Moreover, population diversity can be improved by incorporating the selection of simulated annealing that can reduce the chances of local optima tarp. Therefore, an effective search technique based on a random mutation search along with simulated annealing-based selection is developed. Here, five modified MHs, i.e. the modified DA (MDA), modified ALO (MALO), modified WOA (MWOA), modified HTS (MHTS), and modified TLBO (MTLBO) algorithms, using random mutation search phase and SA-based selection are proposed. The developed techniques are implemented on three standard test problems where dynamic and static constraints and multiple load cases are imposed. The comparative performance of the proposed algorithms and their original algorithms is carried out. An empirical evaluation was done using Friedman rank, and the respective algorithms ranks are assigned. The findings reveal that the new technique results in significant performance enhancement of the various MHs by synchronizing the diversification and intensification of search. [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]

In this article, we are presenting a new nature-inspired metaheuristic optimization algorithm that we call the Mycorrhiza Tree Optimization Algorithm (MTOA) focused on solving optimization problems. The algorithm is inspired by the relationship between trees and the Mycorrhiza Network (MN). In this relationship, there are interactions such as defense, communication, resource exchange and habitat colonization between the Trees and the MN. The Lotka–Volterra continuous systems (Predator–Prey, Cooperative and Competitive Models) were used for the design of the algorithm. To verify the efficiency of the algorithm, experiments with 36 mathematical functions were performed, but we are only presenting results of the functions with which comparisons were made with other methods. Hypothesis tests were also carried out, where in most of the results the MTOA algorithm was better by 70% and only in the experiments in 100 dimensions it was better by 60%. We believe that the algorithm has the potential to solve problems in control and optimization of neural networks, for which experimentation will be done as future work. [ABSTRACT FROM AUTHOR]

Nowadays, a great deal of attention is paid to metaheuristic algorithms to reach the approximate solution in an acceptable computational time. As one of the recent-developed successful metaheuristics, Social Engineering Optimizer (SEO) algorithm is according to the inspiration of the rules of social engineering to solve approximate optimization problems. In this research, a Modified Social Engineering Optimizer algorithm (MSEO) by using an adjustment operator is proposed in which there are some assessment criteria for defender and attacker to determine and calculate the weight simultaneously for the first time. This enhancement comprises adding adjustment operators to improve the performance of SEO in terms of search accuracy and running time. Most notably, this operator is utilized to make a better new generation and improve the interaction between the search phases. The adjustment operator strategy is also applied to a novel division based on the best person. As an extensive comparison, the suggested algorithm is tested on fourteen standard benchmark functions and compared with ten well-established and recent optimization algorithms as well as the main version of the SEO algorithm. This algorithm is also tested for sensitivities on the parameters. In this regard, a set of engineering applications were provided to prove and validate the MSEO algorithm for the first time. The experimental outcomes show that the suggested algorithm produces very accurate results which are better than the SEO and other compared algorithms. Most notably, the MSEO provides a very competitive output and a high convergence rate. [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]

The present research proposes a new particle swarm optimization-based metaheuristic algorithm entitled "search in forest optimizer (SIFO)" to solve the global optimization problems. The algorithm is designed based on the organized behavior of search teams looking for missing persons in a forest. According to SIFO optimizer, a number of teams each including several experts in the search field spread out across the forest and gradually move in the same direction by finding clues from the target until they find the missing person. This search structure was designed in a mathematical structure in the form of intragroup search operators and transferring the expert member to the top team. In addition, the efficiency of the algorithm was assessed by comparing the results to the standard representations and a problem with the genetic, grey wolf, salp swarm, and ant lion optimizers. According to the results, the proposed algorithm was efficient for solving many numerical representations, compared to the other algorithms. [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]

Since real-world problems are quite complex, developing more effective methods to optimize them is taken into consideration at the present age. One of the most popular strategies to solve such problems is nature-inspired metaheuristic algorithms which have presented promising solutions in different fields. The JAYA algorithm is an innovative and promising technique for solving a wide range of optimization problems. Hence, this investigation provides a new hybrid algorithm based on JAYA and crow search algorithm (CSA), known as HJCSA, to enhance its performance. In other words, the mechanism of updating position in CSA is integrated with JAYA algorithm to reinforce the convergence and search capabilities of JAYA algorithm. The proposed hybrid algorithm is evaluated through solving 20 well-known benchmark functions, and obtained results are compared with various up-to-date algorithms, including CSA, JAYA, particle swarm optimization (PSO), dragonfly algorithm (DA), grasshopper optimization algorithm (GOA), moth-flame optimization (MFO), and sine–cosine algorithm (SCA). In addition, a real-world engineering problem is optimized by the proposed optimization approach and compared with other published works. The results showed that the suggested optimization algorithm is much more vigorous in escaping from local minima, possessing better convergence, and discovering more accurate solutions. [ABSTRACT FROM AUTHOR]

To solve global optimization problems, nature-inspired metaheuristics have emerged as the best option in many cases. The researchers have mimicked or mapped the optimal behaviors of living organisms, natural phenomena and human behaviors to solve optimization problems. Whenever a new algorithm is proposed, its exploration, exploitation and convergence capabilities are tested; however, in this study, it is highlighted that a metaheuristic should also exhibit invariance to function translation/shifting. Whale optimization algorithm (WOA) is a well-reputed and highly referred algorithm in the literature; however, we find that its performance is strongly affected under certain circumstances involving function shifting. Our analysis reveals that WOA performs really well when the global optimum is situated at the origin ( 0 , 0 , ... , 0 ); on the contrary, the performance amazingly degrades whenever the global optimum is situated away from the origin. Moreover, in most cases, our findings reveal that the performance degradation is directly proportional to the distance of the global optimum from the origin. Furthermore, the root cause of the problem is discovered and discussed. Finally, it is suggested how WOA can be made shift-invariant. [ABSTRACT FROM AUTHOR]

Past present future (PPF) is a new stochastic optimization algorithm inspired by the phenomena of the way an individual learns from a successful person in society. PPF is based on the concept of "future improvement of a person's life depending on his/her past experience and present work." The influence of successful persons also affects the improvement of the future life of an individual. This work develops a mathematical model for PPF following the above facts. In this new algorithm, the population is divided into subpopulations and a switching mechanism is followed among the subpopulations to track the change in optimal positions of an individual thereby accelerating the convergence rate. In addition, this switching mechanism also prevents pre-mature convergence. PPF was found to possess low computational complexity with fast convergence characteristics. The proposed PPF is compared with 41 up-to-date meta-heuristic algorithms taking an extensive set of benchmark functions to verify the efficiency. In addition, five classical engineering design problems are simulated to estimate the efficacy of the PPF algorithm in optimizing engineering problems. The results confirm the superior performance of the proposed algorithm to get the optimal solution with less iteration and have shown the best competitive performance compared to all other algorithms. [ABSTRACT FROM AUTHOR]

The efficiency of tunnel boring machines (TBMs) in underground projects has great significance for the mining and tunneling industries, demanding a reliable estimation of the TBM's performance in different geotechnical conditions. The current research work attempted to suggest an optimal predictor model of TBM performance as a reliable alternative to experimental and numerical techniques. To achieve this target, three data-mining techniques, namely neural network (NN), gene expression programming (GEP), and multivariate adaptive regression splines (MARS), were employed for modelling the TBM performance, and then the most robust predictive model was optimized via a metaheuristic search method known as whale optimization algorithm (WOA). For the modelling purpose, an experimental database was compiled by performing a field assessment program in a tunneling project in Malaysia and then conducting laboratory testing on the derived rock specimens. Based on the measured experimental data, the six most influential parameters were identified and served as model inputs to predict penetration rate (PR). In order to indicate the capability of the developed GEP and NN models, a stepwise linear regression model, i.e., MARS, was designed for PR prediction as well. The predictive capacity of the constructed models was quantified using a series of statistical indices, i.e., root mean squared error (RMSE), determination coefficient (R2) and variance account for (VAF). Based on the computed indices for testing records, both the proposed GEP and NN models (with RMSE values of 0.1882 and 0.2120 and R2 values of 0.9058 and 0.8735, respectively) yielded more accurate predictive results than the MARS model with RMSE of 0.2553 and R2 of 0.8346. Hence, by achieving the most robust performance compared to the rest, GEP-based model can provide a new practical equation with a high level of accuracy. In other part of this study, the six input parameters of the GEP model and its equation were, respectively, defined as decision variables and objective function for the WOA technique to find the optimum values of PR. As a consequence of optimizing the GEP equation, the maximum value of PR rose from 3.75 m/h to 4.022 m/h, equivalent to an increase of 7.25% in PR value. The findings of this study verified the applicability of the proposed hybrid GEP and WOA approach in the site investigation phase of tunneling projects constructed by TBMs. [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]

The major purpose of this article is to enhance the performance of GOA algorithm by integrating a new mutation operator to the standard GOA algorithm. A series of six different variants of enhanced GOA is proposed by integrating GOA with six different variants of the mutation operator. The new enhanced metaheuristic optimization method is called EGOAs. EGOA aims to address the problems of slow convergence and trapping into local optima, by achieving a good balance between exploration and exploitation, using a special mutation operator that enhances the diversity of the standard GOA, to find the best solution for global optimization problems. The implementation process for enhancing the GOA algorithm is presented and the effectiveness of the enhanced algorithm is evaluated against 60 of the optimization benchmark functions, and compared to that of the standard GOA, as well as to other metaheuristic optimization algorithms. The performance of EGOAs was compared with the other improved methods based on GOA. Experimental results show that EGOAs is clearly superior to the standard GOA algorithm, as well as to other well-known algorithms, in terms of achieving the best optimal value, convergence speed, and avoiding local minima, which makes EGOAs a promising addition to the arsenal of metaheuristic algorithms. [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]

Aiming at the shortcomings of quantum-behaved particle swarm optimization algorithm (QPSO), an improved quantum-behaved particle swarm optimization algorithm (IQPSO) is put forward, and the improved algorithm is applied in solving a kind of fuzzy portfolio selection problems. Firstly, a kind of portfolio models with fuzzy return rates and background risk is established followed by some necessary preparations of fuzzy theory. Then, in the improved algorithm, hybrid probability distribution strategy and contraction–expansion coefficient with nonlinear structure are chosen to enhance particle's exploration ability, and premature prevention mechanism is used to maintain population diversity. Furthermore, the experimental results on 16 benchmark functions show that IQPSO has better convergence and robustness than PSO with inertia weight, QPSO and QPSO with a hybrid probability distribution in most cases. Finally, when solving a fuzzy portfolio model, IQPSO provides comparable and superior results compared with the other metaheuristics. [ABSTRACT FROM AUTHOR]

One of the methods for solving optimization problems is applying metaheuristic algorithms that find near to optimal solutions. Dragonfly algorithm is one of the metaheuristic algorithms which search problem space by the inspiration of hunting and emigration behavior of dragonflies in nature. However, it suffers from the premature convergence of the population to an undesirable point in the detection ability (global search). In this research, an improved dragonfly algorithm called BMDA (applying Biogeography-based algorithm, Mexican hat wavelet, and Dragonfly algorithm) is presented to resolve the premature convergence in high workloads by creating a mutation phase based on the combination of the biogeography-based optimization (BBO) migration process and the Mexican hat wavelet transform in dragonfly algorithm (DA). The algorithm was evaluated for the mean error in comparison with standard dragonfly algorithm (DA), Memory-based Hybrid Dragonfly Algorithm (MHDA), chaotic dragonfly algorithm version 9 (CDA9), Adaptive_DA algorithm, bat algorithm (BAT), particle swarm optimization algorithm (PSO), raven roosting optimization (RRO) and whale optimization algorithm (WOA) using the CEC2017 benchmark functions. The implementation results of the proposed BMDA algorithm applying different benchmark functions outweighed the DA-based algorithm, MHDA algorithm, CDA9 algorithm, Adaptive_DA algorithm, BAT algorithm, PSO algorithm, RRO, and WOA algorithms in terms of mean error. [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]

Given the outstanding effectiveness and efficiency performance in different fields such as image processing and energy dispatching, the virus optimization algorithm (VOA), a newly developed metaheuristic for general optimization purposes, has been further improved. Similar to other metaheuristic methods, VOA performance to some degree relies on proper parameter settings, which may require large numbers of experiments to determine. Therefore, this study proposes a self-adaptive version of VOA (SaVOA) to decrease the number of controllable parameters in the algorithm and thus reduce the time needed to determine proper parameter values by any sort of experimental design process. Having an SaVOA ensures the ease access of the algorithm for different types of continuous domain problems, whereas previous different optimization problems may have needed different parameter settings. To perform the comparison, SaVOA is tested by optimizing the same set of benchmark functions used when proposing the original VOA. Computational results indicate some major advances were achieved by the SaVOA in addition to competitive results obtained. Most importantly, SaVOA proved its superiority on functions where the original VOA was not powerful enough to perform well, such as Rosenbrock, Schwefel, Drop Wave, Levy, and Easom's functions. In terms of implementation, the number of controllable parameters in SaVOA was greatly reduced to only one—the stopping criterion. This promises a significant improvement in the utility of SaVOA for any type of continuous domain optimization problem. [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]

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]

The building of a system for exploring the opinions of users that are made in the blog posts, tweets, reviews or comments regarding a particular topic, policy or a product is known as sentiment analysis. The primary aim of this is the determination of the user attitude regarding a certain topic. The harmony search algorithm has proved to be extremely useful in a varied range of problems in optimization. This shows better performance compared to the other techniques of optimization. Another very powerful technique that is applied to machine learning which is now getting extremely popular is gradient boosting. There are several tree parameters which have been optimized for the random forest and the gradient boosting machine that make use of the harmony search algorithm. [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]

Meta-heuristic algorithms are divided into two categories: biological and non-biological. Biological algorithms are divided into evolutionary and swarm-based intelligence, where the latter is divided into imitation based and sign based. The whale algorithm is a meta-heuristic biological swarm-based intelligence algorithm (based on imitation). This algorithm suffers from the early convergence problem which means the population convergences early to an unfavorable optimum point. Usually, the early convergence occurs because of the weakness in exploration capability (global search). In this study, an optimized version of the whale algorithm is proposed that introduces a new idea in grouping of whales (called GWOA) to overcome the early convergence problem. The proposed whale optimization algorithm is compared with the standard whale algorithm (WOA), CWOA improved whale algorithm, particle swarm optimization, and BAT algorithms applying CEC2017 functions. The results of the experiments show that the proposed method applying Friedman's test on 30 standard benchmark functions has a better performance than the other baseline algorithms. [ABSTRACT FROM AUTHOR]