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The Jaya Algorithm is a relatively new population-based optimization, which has become a progressively valuable tool in swarm intelligence. The Jaya algorithm incorporates the survival of the fittest principle alike evolutionary algorithm by its victorious nature as well as the ideal of an inducement towards a global optimal, which represents its swarm intelligence nature. Nevertheless, it has been applied in various areas of optimization, mainly in engineering practice, which is discussed and abridged based on each problem's domain.The Jaya optimization's vast applicability can be explained by its ability to work without any algorithm-specific parameters. The successfully solved problems may also use some of this meta-heuristic's variants, in which the algorithm has been modified or hybridized. This paper focuses on a comprehensive review, as well as a bibliometric study of the Jaya algorithm, to imply its versatility. Hence, this study is likely to emphasize this optimization's abilities, inspiring new researchers to make use of this simple and efficient algorithm for problem-solving. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are widely known as efficient solutions for solving problems of optimization. These algorithms supply powerful instruments with significant engineering, industry, and science applications. The Quantum-inspired metaheuristic algorithms were developed by integrating Quantum Computing (QC) concepts into metaheuristic algorithms. The QC-inspired metaheuristic algorithms solve combinational and numerical optimization problems to achieve higher-performing results than conventional metaheuristic algorithms. The QC is used more than any other strategy for accelerating convergence, enhancing exploration, and exploitation, significantly influencing metaheuristic algorithms' performance. The QC is a new field of research that includes elements from mathematics, physics, and computing. QC has attracted increasing attention among scientists, technologists, and industrialists. During the current decade, it has provided a research platform for the scientific, technical, and industrial areas. In QC, metaheuristic algorithms can be improved by the parallel processing feature. This feature helps to find the best solutions for optimization problems. The Quantum-inspired metaheuristic algorithms have been used in the optimization fields. In this paper, a review of different usages of QC in metaheuristics has been presented. This review also shows a classification of the Quantum-inspired metaheuristic algorithms in optimization problems and discusses their applications in science and engineering. This review paper's main aims are to give an overview and review the Quantum-inspired metaheuristic algorithms applications. [ABSTRACT FROM AUTHOR]

This paper presents a new metaheuristic algorithm by enhancing one of the recently proposed optimizers named Aquila optimizer (AO). The enhanced AO (enAO) algorithm is constructed by employing a novel modified opposition-based learning (OBL) mechanism and Nelder-Mead (NM) simplex search method. The novel modified OBL aids the AO in further diversification while the NM method increases the intensification. The enAO algorithm is first demonstrated to have more extraordinary ability than the original AO algorithm by employing challenging benchmark functions from the CEC 2019 test suite. The constructed enAO algorithm is proposed to design a PID plus second-order derivative (PIDD2) controller used in an automatic voltage regulator (AVR) system. To reach better efficiency, a novel objective function is also proposed in this paper. Initially, the proposed enAO-PIDD2 approach is demonstrated to be superior in terms of transient and frequency responses along with robustness and disturbance rejection compared to other available and best performing PID, fractional order PID (FOPID), PID acceleration (PIDA), and PIDD2 controllers tuned with different practical algorithms. Moreover, the superior performance of the proposed approach is also demonstrated comparatively using other available techniques for the AVR system reported in the last six years. [ABSTRACT FROM AUTHOR]

The geometric design and shape optimization of complex surfaces are pivotal and knotty techniques in computer aided geometric design (CAGD), and widely used in many complex product manufacturing fields involving surfaces modeling, e.g., for ships, aircraft wing, automobiles, etc. In this paper, an enhanced golden jackal optimization (GJO) algorithm is used to optimize the shape of complex composite shape-adjustable generalized cubic Ball (CSGC-Ball, for short) surfaces. Firstly, the shape design of CSGC-Ball surfaces is mathematically an optimization problem that can be efficiently dealt with by meta-heuristic algorithms. In this regard, an enhanced GJO (EGJO), combined with opposition-based learning, spring vibration-based adaptive mutation and binomial-based cross-evolution strategy, is developed to improve the convergence speed and calculation accuracy of the original GJO. The performance of EGJO is assessed on 23 benchmark test functions, IEEE CEC-2019 and 4 actual engineering optimization problems, and the competition and practicability of EGJO algorithm are confirmed. Secondly, the CSGC-Ball surfaces with global and local shape parameters is constructed based on a class of cubic generalized Ball basis functions, and then the conditions of G1 and G2 continuity for the surfaces are derived. The shapes of CSGC-Ball surfaces can be adjusted and optimized expediently by utilizing their shape parameters. Finally, the minimum energy-based shape optimization models of CSGC-Ball surfaces with 1th-order and 2th-order geometric continuity are established, respectively. Furthermore, the proposed EGJO is utilized to solve the established optimization models, and the CSGC-Ball surfaces with minimum energy are obtained. Four representative examples are given to demonstrate the excellence and effectiveness of EGJO in solving the shape optimization problems of complex CSGC-Ball surfaces. [ABSTRACT FROM AUTHOR]

The smooth path planning for mobile robots is attracted particular research attention. In this paper, an enhanced slime mould algorithm called HG-SMA is proposed to solve a new smooth path planning model based on the Said-Ball curve. Firstly, the enhanced algorithm is constructed by adding a hierarchical guided strategy. This strategy considers the characteristics of individuals with different fitness values, and divides the slime mould population into two hierarchies. And corresponding strategies are applied to different hierarchies to improve the quality of solutions. To validate the performance of the proposed HG-SMA algorithm, it is compared with other improved slime mould algorithms and classical meta-heuristic algorithms on test functions of CEC2017 and CEC2019 suites. Results show HG-SMA performs best on 74.36 and 79.49% of all 39 functions when compared with other improved SMA algorithms and classical optimization algorithms, respectively, which illustrates it is superior to others in solution accuracy, stability and convergence speed. Secondly, by regarding the control points as variables, a novel smooth path planning model based on Said-Ball curve is established to generate the feasible path for mobile robots. Compared with other traditional path planning approaches (A*, RRT and Informed RRT*), the Said-Ball curve-based approach can construct paths with higher smoothness, and has advantages in calculation speed compared with Bézier curve-based approach. Finally, the proposed HG-SMA is employed to solve the established optimization model based on Said-Ball curve, named the Said-Ball + HG-SMA approach. In three designed workplaces, HG-SMA also has advantages in generating feasible paths with shorter lengths and higher smoothness when compared with the original SMA and other classical algorithms. [ABSTRACT FROM AUTHOR]

The slime mould algorithm (SMA) is a new meta-heuristic algorithm recently proposed. The algorithm is inspired by the foraging behavior of polycephalus slime moulds. It simulates the behavior and morphological changes of slime moulds during foraging through adaptive weights. Although the original SMA's performance is better than most swarm intelligence algorithms, it still has shortcomings, such as quickly falling into local optimal values and insufficient exploitation. This paper proposes a Gaussian barebone mutation enhanced SMA (GBSMA) to alleviate the original SMA's shortcomings. First of all, the Gaussian function in the Gaussian barebone accelerates the convergence while also expanding the search space, which improves the algorithm exploration and exploitation capabilities. Secondly, the differential evolution (DE) update strategy in the Gaussian barebone, using rand as the guiding vector. It also enhances the algorithm's global search performance to a certain extent. Also, the greedy selection is introduced on this basis, which prevents individuals from performing invalid position updates. In the IEEE CEC2017 test function, the proposed GBSMA is compared with a variety of meta-heuristic algorithms to verify the performance of GBSMA. Besides, GBSMA is applied to solve truss structure optimization problems. Experimental results show that the convergence speed and solution accuracy of the proposed GBSMA are significantly better than the original SMA and other similar products. [ABSTRACT FROM AUTHOR]

COVID-19 disease, which highly affected global life in 2020, led to a rapid scientific response. Versatile optimization methods found their application in scientific studies related to COVID-19 pandemic. Differential Evolution (DE) and Particle Swarm Optimization (PSO) are two metaheuristics that for over two decades have been widely researched and used in various fields of science. In this paper a survey of DE and PSO applications for problems related with COVID-19 pandemic that were rapidly published in 2020 is presented from two different points of view: 1. practitioners seeking the appropriate method to solve particular problem, 2. experts in metaheuristics that are interested in methodological details, inter comparisons between different methods, and the ways for improvement. The effectiveness and popularity of DE and PSO is analyzed in the context of other metaheuristics used against COVID-19. It is found that in COVID-19 related studies: 1. DE and PSO are most frequently used for calibration of epidemiological models and image-based classification of patients or symptoms, but applications are versatile, even interconnecting the pandemic and humanities; 2. reporting on DE or PSO methodological details is often scarce, and the choices made are not necessarily appropriate for the particular algorithm or problem; 3. mainly the basic variants of DE and PSO that were proposed in the late XX century are applied, and research performed in recent two decades is rather ignored; 4. the number of citations and the availability of codes in various programming languages seems to be the main factors for choosing metaheuristics that are finally used. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms based on intelligent rules have been successfully developed and applied to solve many optimization areas over the past few decades. The sine–cosine algorithm (SCA) imitates the behaviour of transcendental functions while the sine and cosine functions are presented to explore and exploit the search space. SCA starts by random population and executes iterative evolution processes to update the standard evolutionary algorithm's destination or the best location. SCA used linear transition rules to balance the exploration and exploitation searches while searching for the best or optimal solutions. Since Mirjalili proposed it in 2016, SCA has attracted many researchers' attention to deal with several optimization problems in many fields due to its strengths in solving optimization tasks that include the simple concept, easiness of implementation, and rapid convergence. This paper aims to provide researchers with a relatively comprehensive and extensive overview of the Sine–Cosine optimization algorithm in the literature to inspire further research. It examines the available publications, including improvements, binary, chaotic, hybridizations, multi-objective variants, and different applications. Some optimization formulations regarding single-objective optimization problems, multi-objective optimization problems, binary-objective optimization problems, and more classifications regarding the optimization types are discussed. An extensive bibliography is also included. [ABSTRACT FROM AUTHOR]

Structural engineering is focused on the safe and efficient design of infrastructure. Projects can range in size and complexity, many requiring massive amounts of materials and expensive construction and operational costs. Therefore, one of the primary objectives for structural engineers is a cost-effective design. Incorporating optimality criteria into the design procedure introduces additional complexities that result in problems that are nonlinear, nonconvex, and have a discontinuous solution space. Population-based optimization algorithms (known as metaheuristics) have been found to be very efficient approaches to these problems. Many researchers have developed and applied state-of-art metaheuristics to automate and optimize the design of real-world civil engineering problems. While there is a large body of published papers in this area, there are few comprehensive reviews that list, summarize, and categorize metaheuristic optimization in structural engineering. This paper provides an extensive survey of a wide range of metaheuristic techniques to structural engineering optimization problems. Also, information is provided on available structural engineering benchmark problems, the formulation of different objective functions, and the handling of various types of constraints. The performance of different optimization techniques is compared for many benchmark problems. [ABSTRACT FROM AUTHOR]

Sine Cosine Algorithm (SCA) is a recent meta-heuristic algorithm inspired by the proprieties of trigonometric sine and cosine functions. Since its introduction by Mirjalili in 2016, SCA has attracted great attention from researchers and has been widely used to solve different optimization problems in several fields. This attention is due to its reasonable execution time, good convergence acceleration rate, and high efficiency compared to several well-regarded optimization algorithms available in the literature. This paper presents a brief overview of the basic SCA and its variants divided into modified, multi-objective, and hybridized versions. Furthermore, the applications of SCA in several domains such as classification, image processing, robot path planning, scheduling, radial distribution networks, and other engineering problems are described. Finally, the paper recommended some potential future research directions for SCA. [ABSTRACT FROM AUTHOR]

This paper proposes a novel nature-inspired swarm-based optimization algorithm called elk herd optimizer (EHO). It is inspired by the breeding process of the elk herd. Elks have two main breeding seasons: rutting and calving. In the rutting season, the elk herd splits into different families of various sizes. This division is based on fighting for dominance between bulls, where the stronger bull can form a family with large numbers of harems. In the calving season, each family breeds new calves from its bull and harems. This inspiration is set in an optimization context where the optimization loop consists of three operators: rutting season, calving season, and selection season. During the selection season, all families are merged, including bulls, harems, and calves. The fittest elk herd will be selected for use in the upcoming rutting and calving seasons. In simple words, EHO divides the population into a set of groups, each with one leader and several followers in the rutting season. The number of followers is determined based on the fitness value of its leader group. Each group will generate new solutions based on its leader and followers in the calving season. The members of all groups including leaders, followers, and new solutions are combined and the fittest population is selected in the selection season. The performance of EHO is assessed using 29 benchmark optimization problems utilized in the CEC-2017 special sessions on real-parameter optimization and four traditional real-world engineering design problems. The comparative results were conducted against ten well-established metaheuristic algorithms and showed that the proposed EHO yielded the best results for almost all the benchmark functions used. Statistical testing using Friedman's test post-hocked by Holm's test function confirms the superiority of the proposed EHO when compared to other methods. In a nutshell, EHO is an efficient nature-inspired swarm-based optimization algorithm that can be used to tackle several optimization problems. [ABSTRACT FROM AUTHOR]

Many swarm intelligence techniques are facing rigorous challenges since they cannot exploit useful information well during the evolutionary procedure. To remedy this issue, this paper raises a reinforced JAYA algorithm (QLJAYA) that employs the Q-learning and gradient search scheme. In QLJAYA, to balance convergence and diversity, a modified search formula and gradient search scheme are adaptively selected to generate solutions under the control of Q-learning. In addition, to strengthen the rotational invariance of JAYA, the covariance matrix learning strategy is adopted to construct an eigen coordinate system for each solution. Experimental simulations on CEC2017 and CEC2019 test suites and application to identify parameters of photovoltaic systems suggest that QLJAYA can exhibit a better or at least competitive overall performance compared to several typical JAYA variants and other well-known metaheuristics. The source code of QLJAYA is publicly available at https://github.com/denglingyun123/QLJAYA. [ABSTRACT FROM AUTHOR]

This paper designed an advanced hybrid algorithm (haDEPSO) to solve the optimization problems, based on multi-population approach. It integrated with suggested advanced DE (aDE) and PSO (aPSO). Where in aDE a novel mutation strategy and crossover probability along with the slightly changed selection scheme are introduced, to avoid premature convergence. And aPSO consists of the novel gradually varying inertia weight and acceleration coefficient parameters, to escape stagnation. So, convergence characteristic of aDE and aPSO provides different approximation to the solution space. Thus, haDEPSO achieve better solutions due to integrating merits of aDE and aPSO. Also in haDEPSO individual population is merged with other in a pre-defined manner, to balance between global and local search capability. The algorithms efficiency is verified through 23 basic, 30 CEC 2014 and 30 CEC 2017 test suite and comparing the results with various state-of-the-art algorithms. The numerical, statistical and graphical analysis shows the effectiveness of these algorithms in terms of accuracy and convergence speed. Finally, three real world problems have been solved to confirm problem-solving capability of proposed algorithms. All these analyses confirm the superiority of the proposed algorithms over the compared algorithms. [ABSTRACT FROM AUTHOR]

This work presents a new nature-inspired meta-heuristic algorithm named spider wasp optimization (SWO) algorithm, which is based on replicating the hunting, nesting, and mating behaviors of the female spider wasps in nature. This proposed algorithm has various unique updating strategies, making it applicable to a wide range of optimization problems with different exploration and exploitation requirements. The proposed SWO is compared with nine newly published and well-established metaheuristics over four different benchmarks: (1) Standard benchmark, including 23 unimodal and multimodal test functions; (2) test suite of CEC2017, (3) test suite of CEC2020, and (4) test suite of CEC2014 to validate its reliability. Moreover, two classical engineering design problems, namely, welded bean and pressure vessel designs, and parameter estimation of the single-diode, double-diode, and triple-diode photovoltaic models are used to further evaluate the performance of SWO in optimizing real-world optimization problems. Experimental findings demonstrate that SWO is more competitive compared with the state-of-art meta-heuristic methods for four validated benchmarks and superior to all observed real-world optimization problems. Specifically, SWO achieves an overall effective percentage of 78.2% on the standard benchmark, 92.31% on CEC2014, 77.78% on CEC2017, 60% on CEC2020, and 100% on real-world problems. The source code of SWO is publicly available at https://www.mathworks.com/matlabcentral/fileexchange/126010-spider-wasp-optimizer-swo. [ABSTRACT FROM AUTHOR]

The grasshopper optimization algorithm (GOA), which is one of the recent metaheuristic optimization algorithms, mimics the natural movements of grasshoppers in swarms seeking food sources. Some deficiencies have existed in the original GOA such as slow convergence speed, and the original GOA may get quickly stuck into local solutions facing some complex. For tackling these drawbacks of the original GOA, enhanced versions of GOA have been proposed to deal with the optimization problems more effectively. In the current study, two strategies have been integrated into GOA: the grouping mechanism of non-linear 'c' parameters and the mutation mechanism. Moreover, two different groups of non-linear 'c' parameters have been suggested in the grouping mechanism. Incorporating the grouping mechanism into GOA can update the grasshoppers' positions within a limited local area, whereas the diversity of agents can be improved by integrating the mutation mechanism. Eight Novel-Variants GOA (NVGOAs) are proposed to address the deficiencies of the original GOA. Where two variants NVGOA1_1 and NVGOA2_1 represent the impact of each proposed group of 'c' parameters. Another two variants NVGOA3 and NVGOA4 represent the impact of the mutation mechanism with two different values of probability. Moreover, four variants: NVGOA1_2, NVGOA1_3, NVGOA2_2, and NVGOA2_3 represent the combination of the two proposed mechanisms. First, the comparison between the performance of the proposed variants and the original GOA has been conducted. Then, for validation of the efficiency of the proposed NVGOAs, the performance of the best-recorded NVGOA variants has been tested against the 29 CEC-2017 benchmark functions and compared with six state-of-the-art optimization algorithms based on the mean and the standard deviation metrics. Moreover, the Wilcoxon Signed-Rank test has been employed to exhibit the efficiency of the proposed variants. As well comparative analysis with previous enhancements of GOA has been conducted against the best-recorded NVGOA variants. Also, conducting a dimension study between the best-recorded chaotic previous variants against the best-recorded proposed NVGOA variants has revealed the superiority of NVGOAs. The results of all these analyses demonstrated the success and efficiency of the proposed NVGOA variants to solve numerical optimization problems. Concerning demand side management in smart grids, the proposed NVGOA variants have been applied to schedule the loads in three areas: residential, commercial, and industrial to decrease the daily operating costs and peak demand. The results show that the peak demand is reduced by 23.9%, 17.6%, and 9.2% in residential areas, commercial areas, and industrial areas respectively. Also, the operating cost decreased by 7.25%, 9.2%, and 18.89% in residential, commercial, and industrial areas, respectively. Finally, the overall results show that the proposed NVGOA algorithms are effective solutions to address the flaws of the original version of GOA and can get high-quality solutions for different optimization problems. [ABSTRACT FROM AUTHOR]

The colony Predation Algorithm (CPA) has been proven to be one of the heuristic algorithms that can efficiently solve global optimization problems. Balancing the paradox between exploration and exploitation capabilities while mitigating premature convergence are two key subjects that need to be addressed in CPA research. To effectively alleviate these problems, this study proposes a CPA variant named Covariance Gaussian cuckoo Colony Predation Algorithm (CGCPA). Specifically, the designed gaussian cuckoo variable dimensional strategy is used to decentralize the agent population in CPA to enhance the search agents' population diversity and global search ability. The covariance matrix adaptation evolution strategy is used to enhance the convergence speed of the evolutionary agents and the ability to capture the global optimal solution at a later stage. This study subjects CGCPA to competitive experiments with ten basic metaheuristics and ten state−of−the−art algorithms on the IEEE CEC 2017 function test suite. Experimental results confirm that CGCPA outperforms several state−of−the−art DE variants and the latest proposed algorithms in terms of convergence speed and accuracy. In addition, this study proposes a discrete binary feature selection method to better select features in medical data classification named BCGCPA. Its feature selection capability is evaluated in detail on 12 high−dimensional biomedical datasets in the UCI machine learning repository. BCGCPA achieves the lowest classification error rate on all 12 high−dimensional datasets, realizing the best feature selection classification accuracy. BCGCPA can be an efficient pre−processing tool for the dimensionality reduction of high−dimensional biomedical data. It has crucial applications in search space optimization and feature selection of medical datasets. [ABSTRACT FROM AUTHOR]

Numerous optimization problems can be addressed using metaheuristics instead of deterministic and heuristic approaches. This study proposes a novel population-based metaheuristic algorithm called the Exponential Distribution Optimizer (EDO). The main inspiration for EDO comes from mathematics based on the exponential probability distribution model. At the outset, we initialize a population of random solutions representing multiple exponential distribution models. The positions in each solution represent the exponential random variables. The proposed algorithm includes two methodologies for exploitation and exploration strategies. For the exploitation stage, the algorithm utilizes three main concepts, memoryless property, guiding solution and the exponential variance among the exponential random variables to update the current solutions. To simulate the memoryless property, we assume that the original population contains only the winners that obtain good fitness. We construct another matrix known as memoryless to retain the newly generated solutions regardless of their fitness compared to their corresponding winners in the original population. As a result, the memoryless matrix stores two types of solutions: winners and losers. According to the memoryless property, we disregard and do not memorize the previous history of these solutions because past failures are independent and have no influence on the future. The losers can thus contribute to updating the new solutions next time. We select two solutions from the original population derived from the exponential distributions to update the new solution throughout the exploration phase. Furthermore, EDO is tested against classical test functions in addition to the Congress on Evolutionary Computation (CEC) 2014, CEC 2017, CEC 2020 and CEC 2022 benchmarks, as well as six engineering design problems. EDO is compared with the winners of CEC 2014, CEC 2017 and CEC 2020, which are L-SHADE, LSHADE−cnEpSin and AGSK, respectively. EDO reveals exciting results and can be a robust tool for CEC competitions. Statistical analysis demonstrates the superiority of the proposed EDO at a 95% confidence interval. [ABSTRACT FROM AUTHOR]

The increase in proposed clustering routing protocols for Wireless Sensor Networks has motivated the development of survey studies that provide quick access to clear and meaningful information about state-of-the-art approaches. This review focuses on the most recent clustering routing protocols for WSNs based on metaheuristic techniques. Since there is a lack of survey studies that provide a comprehensive analysis of this field, we present a more in-depth study of different metaheuristic-based strategies mainly for selecting optimal cluster heads. The primary objective of the proposed work is to review approaches that have developed novel cluster-based routing protocols primarily for reducing the energy consumption of WSNs. In this survey, we examine every protocol considering its methodology and properties from the perspective of the metaheuristic community. Additionally, we present a comparative analysis of the reviewed approaches regarding the implemented network structure, the network characteristics, the metaheuristic algorithm used, the proposed search strategy, reported metrics, and the obtained results. [ABSTRACT FROM AUTHOR]

Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms. Several methods have been developed to perform HPO; most of these are focused on optimizing one performance measure (usually an error-based measure), and the literature on such single-objective HPO problems is vast. Recently, though, algorithms have appeared that focus on optimizing multiple conflicting objectives simultaneously. This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms, distinguishing between metaheuristic-based algorithms, metamodel-based algorithms and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) suffers from delayed convergence and stagnation in the local optimal solution, as do most meta-heuristic algorithms. This study proposes a time-based leadership particle swarm-based Salp (TPSOSA) to address the PSO's limitations. The TPSOSA is a novel search technique that addresses population diversity, an imbalance between exploitation and exploration, and the premature convergence of the PSO algorithm. Hybridization in TPSOSA is divided into two stages: The PSO hierarchy of leaders and followers is first represented as a time-varying dynamic structure. Because we need much exploration at the beginning and many exploitative steps at the end, this method raises the number of leaders while decreasing the number of follower particles linearly. In the time-varying form of the PSO (TPSOSA), unlike the PSO, the number of leaders and followers changes over time. PSO's robust search strategy is used to update the leaders' positions. Second, the SSA's powerful exploitation is utilized to update the followers' swarm population position. The purpose of tweaking the particle swarm optimizer algorithm is to aid the fundamental method in avoiding premature convergence and quickly directing the search to the most promising likely search space. The proposed TPSOSA method is tested using the CEC 2017 benchmark, seven CEC2008lsgo test functions with 200, 500, and 1000 decision variables, and 19 datasets (including three high-dimensional datasets and the NSL-KDD Dataset for Intrusion Detection System). In each experiment, TPSOSA is compared to various state-of-the-art metaheuristics methods. Friedman and Wilcoxon rank-sum statistical tests are also used to analyze the data. Experimental data and statistical tests show that the TPSOSA algorithm is very competitive and often superior to the algorithms used in the studies. According to the results, TPSOSA can also find an optimal feature subset that enhances classification accuracy while reducing the number of features employed. [ABSTRACT FROM AUTHOR]

The detection and brain tumor (BT) segmentation and classification are mandatory steps before any radiotherapy or surgery. When performed manually, segmentation is time-consuming and exposed to human errors. Therefore, significant efforts have been made to automate the process. In this study, a proposed automatic discriminative learning-based approach for brain tumor classification and segmentation using a metaheuristic optimizer called Sparrow Search Algorithm (SpaSA). The segmentation process is performed using UNet models (i.e., U-Net, U-Net++, Attention U-Net, and V-net). Additionally, the learning and SpaSA optimization is performed using pre-trained CNN models (i.e., MobileNet, MobileNetV2, MobileNetV3Small, MobileNetV3Large, EfficientNetB0, EfficientNetB1, EfficientNetB2, EfficientNetB3, EfficientNetB4, EfficientNetB5,VGG16, and VGG19). To optimize the training hyperparameters, the SpaSA metaheuristic optimizer is used. The dataset is collected from 6 public sources. Two types of datasets are generated. One with 2-classes and the other with 4-classes. The best-reported scores by U-Net architecture are 99.73% accuracy, 99.93% specificity, 99.35% AUC, 99.78% IoU, and 99.80% Dice for the whole tumor region. For the 2-classes dataset, the best reported overall accuracy from the applied CNN experiments is 99.99% by the MobileNetV3 Large pre-trained model. The average accuracy is 99.92%. Similarly, For the 4-classes dataset, the best reported overall accuracy from the applied CNN experiments is 99.73% by the EfficientNetB2 pre-trained model. The average accuracy is 99.19%. The suggested approach is compared with 11 related studies. [ABSTRACT FROM AUTHOR]

This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future. [ABSTRACT FROM AUTHOR]

A hybrid method for energy management on grid-connected MG system is proposed under this manuscript. Grid-connected MG system takes photovoltaic (PV), wind turbine (WT), battery. The proposed system is an integration of seagull optimization algorithm (SOA) and the radial basic functional neural network (RBFNN), thus it is named SOA-RBFNN. Here, in the grid-connected microgrid configuration, the necessary load demand is always monitored with RBFNN methodology. SOA optimizes the perfect match of the MG taking into account the predictable load requirement. The fuel cost, together with the power variation per hour of the electric grid, the operation and maintenance cost of microgrid system linked with grid, is described. The proposed model runs on the MATLAB/Simulink workstation and efficiency is investigated using existing techniques as AGO-RNN and MBFA-ANN. Statistical analysis, elapsed time, modeling metrics, and determination of optimal sample size for adjustment and validation of proposed and existing technique are evaluated. The efficiency values on the 100, 200, 500, and 1000 trails are 99.7673%, 99.7609%, 99.9099%, and 99.9373%. [ABSTRACT FROM AUTHOR]

Scheduling has an immense effect on various areas of human lives, be it though its application in manufacturing and production industry, transportation, workforce allocation, or others. The unrelated parallel machines scheduling problem (UPMSP), which is one of the various problem types that exist, found its application in many areas like manufacturing and distributed computing. Due to the complexity of the problem, heuristic and metaheuristic methods have dominantly been applied for solving it. Although this problem variant did not receive much attention as other models, recent years saw the increase of research dealing with the UPMSP. During that time, different problem variants, solution methods, and interesting research directions were considered. However, no study provided a systematic overview of the research in which heuristic methods are applied for solving the UPMSP. This comes as a problem since it is becoming difficult to keep track of all the relevant research directions and solution methods considered for this problem. Therefore, the goal of this study is to provide an extensive literature review on the application of heuristic and metaheuristic methods for solving the UPMSP. Each reviewed study is briefly described based on the considered problem and solution method. Additionally, studies dealing with similar problems are grouped together to outline the evolution of the research, and possible areas where further research can be carried out. All studies were systematised and classified into several categories to allow for an easy overview of different problem and solution variants. Finally, recent research trends and possible future directions are also outlined. [ABSTRACT FROM AUTHOR]

Generalized normal distribution optimization (GNDO) inspired by the theory of normal distribution is a recently developed metaheuristic method for global optimization problems. This work presents a novel variant of GNDO, which is called elite-driven generalized normal distribution optimization (EDGNDO). EDGNDO enhances the global search ability of GNDO by the designed search mechanism consisting of three local search operators and three global search operators that are based on two built archives used to save elite individuals. Note that, EDGNDO only needs population size and termination criteria for optimization, which can distinguish it over the most reported metaheuristic methods. The performance of EDGNDO is investigated by the well-known CEC 2017 test suite including three unimodal functions and 27 multimodal functions. Experimental results demenstrate that EDGNDO is obviously better than GNDO and the other five powerful algorithms in terms of solution quality and computational efficiency. In addition, EDGNDO is also used for solving four challenging constrained engineering design problems. Experimental results support the superiority of EDGNDO in solving the four problems. The superiority of EDGNDO in solving complex optimization problems is proven. The source code can be loaded from https://github.com/jsuzyy/EDGNDO. [ABSTRACT FROM AUTHOR]

This study proposes the Fire Hawk Optimizer (FHO) as a novel metaheuristic algorithm based on the foraging behavior of whistling kites, black kites and brown falcons. These birds are termed Fire Hawks considering the specific actions they perform to catch prey in nature, specifically by means of setting fire. Utilizing the proposed algorithm, a numerical investigation was conducted on 233 mathematical test functions with dimensions of 2–100, and 150,000 function evaluations were performed for optimization purposes. For comparison, a total of ten different classical and new metaheuristic algorithms were utilized as alternative approaches. The statistical measurements include the best, mean, median, and standard deviation of 100 independent optimization runs, while well-known statistical analyses, such as Kolmogorov–Smirnov, Wilcoxon, Mann–Whitney, Kruskal–Wallis, and Post-Hoc analysis, were also conducted. The obtained results prove that the FHO algorithm exhibits better performance than the compared algorithms from literature. In addition, two of the latest Competitions on Evolutionary Computation (CEC), such as CEC 2020 on bound constraint problems and CEC 2020 on real-world optimization problems including the well-known mechanical engineering design problems, were considered for performance evaluation of the FHO algorithm, which further demonstrated the superior capability of the optimizer over other metaheuristic algorithms in literature. The capability of the FHO is also evaluated in dealing with two of the real-size structural frames with 15 and 24 stories in which the new method outperforms the previously developed metaheuristics. [ABSTRACT FROM AUTHOR]

In the yard of business organization, due to a major number of competitors and uncertainty of customers' demand, everyone faces some disturbances for smoothly running of their business. To survive in the fierce competition some modern business policies are required and accordingly the formulation of an advanced inventory model is indispensable by handling the uncertainty of customers' demand. In this model formulation, discount facility is considered as a business policy whereas interval valued demand is proposed to tackle the uncertainty of customers' demand. The purpose of this work is to analyze a model with interval valued parameters for deteriorating items considering two situations (with shortages and without shortage) in the discount environment. Moreover, deterioration rate is taken into account as interval valued and unit carrying cost is supposed to be a function of the length of storage time as well as interval valued purchase cost. Also, the interval valued purchase cost is assumed to be a step function (decreasing) of lot size under discount business policy. Considering without shortage and with shortages separately, using parametric approach of interval differential equations, the proposed model is formulated in two different cases. After formulating the proposed model properly, the corresponding profit functions are obtained for two different cases (without shortage and with shortages). In order to optimize the profit, various types of meta-heuristic algorithms (weighted quantum behaved particle swarm optimization and Gaussian quantum behaved particle swarm optimization) are used and then the obtained results are compared. Finally, to justify the reality of the model, numerical illustrations are presented and also post optimality analyses are performed with the changes of known parameters. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs), which are extensions of artificial neural networks, can learn higher levels of feature hierarchy established by lower level features by transforming the raw feature space to another complex feature space. Although deep networks are successful in a wide range of problems in different fields, there are some issues affecting their overall performance such as selecting appropriate values for model parameters, deciding the optimal architecture and feature representation and determining optimal weight and bias values. Recently, metaheuristic algorithms have been proposed to automate these tasks. This survey gives brief information about common basic DNN architectures including convolutional neural networks, unsupervised pre-trained models, recurrent neural networks and recursive neural networks. We formulate the optimization problems in DNN design such as architecture optimization, hyper-parameter optimization, training and feature representation level optimization. The encoding schemes used in metaheuristics to represent the network architectures are categorized. The evolutionary and selection operators, and also speed-up methods are summarized, and the main approaches to validate the results of networks designed by metaheuristics are provided. Moreover, we group the studies on the metaheuristics for deep neural networks based on the problem type considered and present the datasets mostly used in the studies for the readers. We discuss about the pros and cons of utilizing metaheuristics in deep learning field and give some future directions for connecting the metaheuristics and deep learning. To the best of our knowledge, this is the most comprehensive survey about metaheuristics used in deep learning field. [ABSTRACT FROM AUTHOR]