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This paper presents an optimization method for selective harmonic elimination in a cascaded multilevel inverter (MLI) using teaching-learning based optimization (TLBO). The main objective in selective harmonic elimination (SHE) strategy is to eliminate low-order harmonics by solving nonlinear equations and reaching optimal solution, while the fundamental component is satisfied. In this paper, different optimization methods are compared. In this paper, the TLBO as a recently emerged nature-inspired algorithm is presented to provide better results for the SHE in comparison with Genetic algorithm (GA), Particle swarm optimization (PSO), Newton Raphson Method (NR) and Satin Bowerbird algorithm (SBO). For better comparison of those methods and influence of optimal DC source a 7-level inverter is chosen and MATLAB/Simulink software is used for optimization. Simulation results show the superiority of TLBO, higher precision and probability of convergence than other mentioned algorithms. [ABSTRACT FROM AUTHOR]

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

Golden jackal optimization (GJO) is an effective metaheuristic algorithm that imitates the cooperative hunting behavior of the golden jackal. However, since the update of the prey's position often depends on the male golden jackal and there is insufficient diversity of golden jackals in some cases, it is prone to falling into a local optimal optimum. In order to address these drawbacks of GJO, this paper proposes an improved algorithm, called a hybrid GJO and golden sine (S) algorithm (Gold-SA) with dynamic lens-imaging (L) learning (LSGJO). First, this paper proposes novel dual golden spiral update rules inspired by Gold-SA. These rules give GJO the ability to think like a human (Gold-SA), making the golden jackal more intelligent in the process of preying, and improving the ability and efficiency of optimization. Second, a novel nonlinear dynamic decreasing scaling factor is introduced into the lens-imaging learning operator to maintain the population diversity. The performance of LSGJO is verified through 23 classical benchmark functions and 3 complex design problems in real scenarios. The experimental results show that LSGJO converges faster and more accurately than 11 state-of-the-art optimization algorithms, the global and local search ability has improved significantly, and the proposed algorithm has shown superior performance in solving constrained problems. [ABSTRACT FROM AUTHOR]

Snake optimizer (SO) is an optimization algorithm drawn from the reproductive habits of serpents. It exhibits outstanding effectiveness in solving continuous optimization problems. However, SO may face some performance challenges related to its population diversity and early convergence behavior. In this paper, we address the challenges of SO by introducing the Hybrid snake optimizer algorithm (HSOA). HSOA is a novel approach to optimization that incorporates two new optimization techniques into the SO algorithm. First, it incorporates a new opposition-based learning technique called Oppositional-mutual learning into the initialization stage of the SO algorithm. Second, it integrates dynamic polynomial mutation, which is an intelligent mutation method, into the initialization and optimization stages of the SO algorithm. These integrated approaches aim to increase the population's diversity of SO, while improving its searchability during its optimization stage. In power systems, the economic load dispatch (ELD) is an intricate optimization problem that becomes more challenging when the restrictions of the valve point effect (VPE) are incorporated into it. ELD with VPE is non-convex, lacking smoothness, and exhibiting nonlinearity that considers operational limitations expressed as both equality and inequality constraints to generate electricity. The suggested HSOA algorithm underwent evaluation and was compared with 47 renowned optimization algorithms across five real-world ELD problems with different specifications: generators with different unit capacities, transmission losses, prohibited operation zones, and Ramp Rate restrictions. The experimental results demonstrate that HSOA produces competitive solutions for the five real-world ELD problems. In detail, HSOA achieves the top rank in three cases of ELD problems with a 3-unit generator, and it secures the second and third positions in high-dimensional ELD problems with 40-unit and 80-unit generators, respectively. The statistical tests confirm the reliability and efficiency of HSOA. In addition, the effectiveness of HSOA was evaluated using the single-objective IEEE-CEC 2014 functions and compared to the results of eight popular metaheuristic algorithms. The results demonstrate that HSOA is a competitive optimization algorithm capable of solving the functions of IEEE-CEC 2014. [ABSTRACT FROM AUTHOR]

The optimization of industrial processes is a critical task for leveraging profitability and sustainability. To ensure the selection of optimum process parameter levels in any industrial process, numerous metaheuristic algorithms have been proposed so far. However, many algorithms are either computationally too expensive or become trapped in the pit of local optima. To counter these challenges, in this paper, a hybrid metaheuristic called PSO-GSA is employed that works by combining the iterative improvement capability of particle swarm optimization (PSO) and gravitational search algorithm (GSA). A binary PSO is also fused with GSA to develop a BPSO-GSA algorithm. Both the hybrid algorithms i.e., PSO-GSA and BPSO-GSA, are compared against traditional algorithms, such as tabu search (TS), genetic algorithm (GA), differential evolution (DE), GSA and PSO algorithms. Moreover, another popular hybrid algorithm DE-GA is also used for comparison. Since earlier works have already studied the performance of these algorithms on mathematical benchmark functions, in this paper, two real-world-applicable independent case studies on biodiesel production are considered. Based on the extensive comparisons, significantly better solutions are observed in the PSO-GSA algorithm as compared to the traditional algorithms. The outcomes of this work will be beneficial to similar studies that rely on polynomial models. [ABSTRACT FROM AUTHOR]

The photovoltaic(PV) system's efficiency can be improved with the aid of effective solar PV cell modeling. However, flawed solar cell parameters affect PV cell modeling incorrectly. The manufacturers are usually not providing all the necessary data for the exact modeling of PV cells. It is therefore necessary to efficiently predict the parameters of the PV cell. The literature discusses various optimization algorithms, but the suboptimal outcomes are obtained by most of the algorithms due to their convergence toward local minima. This paper introduces a new stochastic optimization algorithm for the estimation of solar PV cell parameters. Therefore, for the evaluation of the solar cell, a new novel chaotic algorithm is introduced in this paper called the chaotic tunicate swarm algorithm (CTSA). The proposed algorithm has a new function called an outstanding mathematical model of adaptive weights to stimulate negative and positive inputs from the spreading wave in order to find the best way to connect food with an excellent exploitation tendency and exploratory ability. For the double‐diode model, the computation time of the chaotic variants (CTSA1‐1.1885, CTSA2‐1.2502, CTSA3‐1.2962, CTSA4‐1.1204, and CTSA5‐1.2214) is better than the rest of the compared algorithms (particle swarm optimization [PSO]‐17.4532, tunicate swarm algorithm (TSA)‐2.5278, Harris hawks optimization [HHO]‐10.8942, ant lion optimizer [ALO]‐15.0214, and atom search optimization [ASO]‐9.5214). For the triple‐diode model, the computation time of the chaotic variants (CTSA1‐1.2658, CTSA2‐1.3520, CTSA3‐1.3056, CTSA4‐1.2290, and CTSA5‐1.3859) is better than the rest of the compared algorithms (PSO‐18.5421, TSA‐3.2568, HHO‐9.8754, ALO‐13.5841, and ASO‐11.8412). The superiority of proposed algorithm is established using ranking test, statistical error analysis, and sensitivity temperature variation. [ABSTRACT FROM AUTHOR]

Wireless powered communication network (WPCN) is a promising technique to resolve the power constraint issue faced by wireless nodes at the same time it also provides green and a safer solution. This paper studies, mutual sharing between WPCN and simultaneous wireless information and power transfer model where optimal resource allocation occurs between licensed/bandwidth group and an unlicensed/power group to enhance the system performance. The main aim of this paper is to maximize system weighted sum rate and minimize power consumption during transmission by optimal time allocation and energy beamforming vector allocation. These multiobjective combinatorial problems are solved using joint metaheuristic particle swarm optimization-cuckoo search algorithm to provide an optimal solution. The results are compared using a conventional mathematical optimization algorithm to set as a baseline for performance parameters. The obtained results demonstrate that the efficient joint metaheuristic approach provides a significant gain in the system performance and also offers a computationally less complex approach for solving a complicated non-convex multiobjective problem of wireless powered network. [ABSTRACT FROM AUTHOR]

When the meta-heuristic algorithm solves the joint resource allocation problem of multi-beam satellites, the computational complexity increases and the algorithm is difficult to converge due to the time delay constraint and capacity constraint. This paper introduced a penalty mechanism in the objective function, and added a penalty value to the objective function of the invalid solution, so that the optimized solution adaptively satisfied these two constraints. Based on this, this paper proposed a joint resource allocation algorithm based on quantum-behaved particle swarm optimization. Simulation results show that the introduction of the penalty strategy solves the problem of difficulty in handling the delay constraint and capacity constraint when applying the meta-heuristic algorithm. The quantum-behaved particle swarm optimization algorithm with penalty mechanism outperforms the existing joint allocation algorithm in terms of allocation fairness index and total system capacity. [ABSTRACT FROM AUTHOR]

Oversampling ratio N and the minority class' nearest neighboring number k are key hyperparameters of synthetic minority oversampling technique (SMOTE) to reconstruct the class distribution of dataset. No optimal default value exists there. Therefore, it is of necessity to discuss the influence of the output dataset on the classification performance when SMOTE adopts various hyperparameter combinations. In this paper, we propose a hyperparameter optimization algorithm for imbalanced data. By iterating to find reasonable N and k for SMOTE, so as to build a balanced and high-quality dataset. As a result, a model with outstanding performance and strong generalization ability is trained, thus effectively solving imbalanced classification. The proposed algorithm is based on the hybridization of simulated annealing mechanism (SA) and particle swarm optimization algorithm (PSO). In the optimization, Cohen's Kappa is used to construct the fitness function, and AdaRBFNN, a new classifier, is integrated by multiple trained RBF neural networks based on AdaBoost algorithm. Kappa of each generation is calculated according to the classification results, so as to evaluate the quality of candidate solution. Experiments are conducted on seven groups of KEEL datasets. Results show that the proposed algorithm delivers excellent performance and can significantly improve the classification accuracy of the minority class. [ABSTRACT FROM AUTHOR]

Four-bar linkages are one of the most widely used mechanisms in industries. This paper presents a comparative study on the accuracy and efficiency of the optimum synthesis of path-generating four-bar linkages using five metaheuristic optimization algorithms. The utilized metaheuristic methods included two swarm intelligence-based algorithms, i.e., particle swarm optimization and hybrid particle swarm optimization, and three evolutionary-based algorithms, i.e., differential evolution, ensemble of parameters and mutation strategies in differential evolution, and linearly ensemble of parameters and mutation strategies in differential evolution. The objective function to be minimized is the sum of squares of the distance between the generated points and the precision points of a coupler point. The optimal design of four-bar linkages must meet the Grashof's criteria and exhibit sequential constraints that can prevent the occurrence of order defect. This study investigated five representative cases of the dimensional synthesis of four-bar path generators with and without prescribed timing and compared the optimal solutions of the utilized five metaheuristic methods to those of previously reported algorithms in literature. The improved metaheuristic methods exhibited superior optimal solution and enhanced reliability compared to the original methods. Moreover, three improved metaheuristic methods were not only easy implemented, but also more efficient for solving the optimal synthesis problems, particularly for high dimensional problems. [ABSTRACT FROM AUTHOR]

Non-Line-of-Sight (NLOS) conditions are created by blocking the direct path between the transmitter and receiver, resulting in an increased signal propagation path. To mitigate the Time of Arrival (TOA) measured errors caused by the NLOS phenomenon in cellular radio positioning, we use the Maximum Likelihood (ML) estimation method in this work. The cost function of the ML estimator is usually a high-dimensional, nonlinear, and multimodal function, where standard deterministic optimization techniques cannot solve such problems in real-time and without significant computing resources. In this paper, effective metaheuristic algorithms based on the enhanced variants of Particle Swarm Optimization (PSO) are applied for the optimal solution of the ML problem and efficiently determine the mobile station location. Time-Varying Acceleration Coefficients (TVAC) are introduced into the standard PSO algorithm to enhance the global search and convergence properties. The resulting algorithm is known as PSO-TVAC. To further improve the performance of the metaheuristic optimization, we suggest adding Chaos Search (CS), Opposition-Based Learning (OBL), and TVAC strategy to the PSO process. The simulation results show that the proposed metaheuristic algorithm named the Chaotic Opposition-based PSO-TVAC (COPSO-TVAC) can reach the Generalized Cramer–Rao Lower Bound (GCRLB) and surpass the original PSO, PSO-TVAC, and the presented conventional optimization algorithms. [ABSTRACT FROM AUTHOR]

Clustering algorithms have been applied to different problems in many different real-word applications. Nevertheless, each algorithm has its own advantages and drawbacks, which can result in different solutions for the same problem. Therefore, the combination of different clustering algorithms (cluster ensembles) has emerged as an attempt to overcome the limitations of each clustering technique. The use of cluster ensembles aims to combine multiple partitions generated by different clustering algorithms into a single clustering solution (consensus partition). Recently, several approaches have been proposed in the literature in order to optimize or to improve continuously the solutions found by the cluster ensembles. As a contribution to this important subject, this paper presents an investigation of five bio-inspired techniques in the optimization of cluster ensembles (Genetic Algorithms, Particle Swarm Optimization, Ant Colony Optimization, Coral Reefs Optimization and Bee Colony Optimization). In this investigation, unlike most of the existing work, an evaluation methodology for assessing three important aspects of cluster ensembles will be presented, assessing robustness, novelty and stability of the consensus partition delivered by different optimization algorithms. In order to evaluate the feasibility of the analyzed techniques, an empirical analysis will be conducted using 20 different problems and applying two different indexes in order to examine its efficiency and feasibility. Our findings indicated that the best population-based optimization method was PSO, followed by CRO, AG, BCO and ACO, for providing robust and stable consensus partitions. [ABSTRACT FROM AUTHOR]

In electronic beam scanning, the number of phase shifters is an obvious challenge. So, there are several methods to reduce the number of phase shifters. The aim of this paper is to investigate the use of the meta-heuristic algorithm to lower the grating lobe level in the subarray antenna. Improve the result obtained by group subarray optimization techniques to determine topology and space between elements, and complex optimization of weight, simultaneously. Uniform subarray and random subarray are analyzed in Matlab to determine the coefficient of excitation by the evolutionary algorithm, as well as swarm and hybrid. The results of the simulation are shown; this method leads to radiation pattern without grating lobe in wide scanning angle. It indicates that there is a possibility of obtaining wide electronic scanning with minimum number of phase shifters and improving result. [ABSTRACT FROM AUTHOR]

In this paper, six metaheuristic algorithms, in the form of artificial bee colony optimization, ant colony optimization, particle swarm optimization, differential evolution, firefly algorithm and teaching-learning-based optimization techniques are applied for parametric optimization of a multi-pass face milling process. Using those algorithms, the optimal values of cutting speed, feed rate and depth of cut for both roughing and finishing operations are determined for having minimum total production time and total production cost. It is observed that the teaching-learning-based optimization algorithm outperforms the others with respect to accuracy and consistency of the derived solutions as well as computational speed. Two statistical tests, i.e. paired t-test and Wilcoxson signed rank test also confirm its superiority over the remaining algorithms. Finally, these metaheuristics are employed for multi-objective optimization of the considered multi-pass milling process while concurrently minimizing both the objectives. [ABSTRACT FROM AUTHOR]

The arithmetic optimization algorithm is a recently proposed metaheuristic algorithm. In this paper, an improved arithmetic optimization algorithm (IAOA) based on the population control strategy is introduced to solve numerical optimization problems. By classifying the population and adaptively controlling the number of individuals in the subpopulation, the information of each individual can be used effectively, which speeds up the algorithm to find the optimal value, avoids falling into local optimum, and improves the accuracy of the solution. The performance of the proposed IAOA algorithm is evaluated on six systems of nonlinear equations, ten integrations, and engineering problems. The results show that the proposed algorithm outperforms other algorithms in terms of convergence speed, convergence accuracy, stability, and robustness. [ABSTRACT FROM AUTHOR]

The PID control algorithm for balancing robot attitude control suffers from the problem of difficult parameter tuning. Previous studies have proposed using metaheuristic algorithms to tune the PID parameters. However, traditional metaheuristic algorithms are subject to the criticism of premature convergence and the possibility of falling into local optimum solutions. Therefore, the present paper proposes a CFHBA-PID algorithm for balancing robot Dual-loop PID attitude control based on Honey Badger Algorithm (HBA) and CF-ITAE. On the one hand, HBA maintains a sufficiently large population diversity throughout the search process and employs a dynamic search strategy for balanced exploration and exploitation, effectively avoiding the problems of classical intelligent optimization algorithms and serving as a global search. On the other hand, a novel complementary factor (CF) is proposed to complement integrated time absolute error (ITAE) with the overshoot amount, resulting in a new rectification indicator CF-ITAE, which balances the overshoot amount and the response time during parameter tuning. Using balancing robot as the experimental object, HBA-PID is compared with AOA-PID, WOA-PID, and PSO-PID, and the results demonstrate that HBA-PID outperforms the other three algorithms in terms of overshoot amount, stabilization time, ITAE, and convergence speed, proving that the algorithm combining HBA with PID is better than the existing mainstream algorithms. The comparative experiments using CF prove that CFHBA-PID is able to effectively control the overshoot amount in attitude control. In conclusion, the CFHBA-PID algorithm has great control and significant results when applied to the balancing robot. [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]

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]

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]

This paper presents a powerful swarm intelligence metaheuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization Algorithm. The original Cat Swarm Optimization suffers from the shortcoming of "premature convergence," which is the possibility of entrapment in local optima which usually happens due to the off balance between exploration and exploitation phases. Therefore, the proposed algorithm suggests a new method to provide a proper balance between these phases by modifying the selection scheme and the seeking mode of the algorithm. To evaluate the performance of the proposed algorithm, 23 classical test functions, 10 modern test functions (CEC 2019) and a real-world scenario are used. In addition, the dimension-wise diversity metric is used to measure the percentage of the exploration and exploitation phases. The optimization results show the effectiveness of the proposed algorithm, which ranks first compared to several well-known algorithms available in the literature. Furthermore, statistical methods and graphs are also used to further confirm the outperformance of the algorithm. Finally, the conclusion and future directions to further improve the algorithm are discussed. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) provides a metaheuristic optimization technique for real-life functions with complex behavior. Inspired by the metaphor of social behavior as exhibited by swarms operating in natural surroundings, the algorithm captures the Emergent Complexity demonstrated by individual components of large groups which work together using simple rules and create complex, yet efficient, adaptive, and self-organizing systems. The paper seeks to rigorously analyze the hyperparameters of the algorithm to find the relative importance of cognitive(personal) vis-à-vis social (swarm) parameters of the algorithm so as to gain a perfect understanding of the importance of exploitation versus exploration parameters thus enabling us to enhance the application efficiency of the algorithm. The paper further suggests and applies a rolling coefficient updating technique to the algorithm parameters so as to use a formal statistical procedure in the initialization and subsequent iterations for updating coefficients rather than leaving it on trial and error or on a practitioner's knowledge. Results suggest that the technique employed provides an enhancement in the efficiency of the algorithm by creating a dynamic Emergent Complexity environment. [ABSTRACT FROM AUTHOR]

The Sine-Cosine algorithm (SCA) is a population-based metaheuristic algorithm utilizing sine and cosine functions to perform search. To enable the search process, SCA incorporates several search parameters. But sometimes, these parameters make the search in SCA vulnerable to local minima/maxima. To overcome this problem, a new Multi Sine-Cosine algorithm (MSCA) is proposed in this paper. MSCA utilizes multiple swarm clusters to diversify & intensify the search in-order to avoid the local minima/maxima problem. Secondly, during update MSCA also checks for better search clusters that offer convergence to global minima effectively. To assess its performance, we tested the MSCA on unimodal, multimodal and composite benchmark functions taken from the literature. Experimental results reveal that the MSCA is statistically superior with regards to convergence as compared to recent state-of-the-art metaheuristic algorithms, including the original SCA. [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]

This paper describes a methodology to design and optimize a controllable pitch propeller suitable for small leisure ship boats. A proper range for design parameters has to be set by the user. An optimization based on the Particle Swarm Optimization algorithm is carried out to minimize a fitness function representing the engine's fuel consumption. The OpenProp code has been integrated in the procedure to compute thrust and torque. Blade's geometry and tables about pitch, thrust and consumption are the main output of the optimization process. A case study has been included to show how the procedure can be implemented in the design process. A case study shows that the procedure allows a designer to sketch a controllable pitch propeller with optimal efficiency; computational times are compatible with the design conceptual phase where several scenarios must be investigated to set the most suitable for the following detailed design. A drawback of this approach is given by the need for a quite skilled user in charge of defining the allowable ranges for design parameters, and the need for data about the engine and boat to be designed. [ABSTRACT FROM AUTHOR]

The difficulty and complexity of the real-world numerical optimization problems has grown manifold, which demands efficient optimization methods. To date, various metaheuristic approaches have been introduced, but only a few have earned recognition in research community. In this paper, a new metaheuristic algorithm called Archimedes optimization algorithm (AOA) is introduced to solve the optimization problems. AOA is devised with inspirations from an interesting law of physics Archimedes' Principle. It imitates the principle of buoyant force exerted upward on an object, partially or fully immersed in fluid, is proportional to weight of the displaced fluid. To evaluate performance, the proposed AOA algorithm is tested on CEC'17 test suite and four engineering design problems. The solutions obtained with AOA have outperformed well-known state-of-the-art and recently introduced metaheuristic algorithms such genetic algorithms (GA), particle swarm optimization (PSO), differential evolution variants L-SHADE and LSHADE-EpSin, whale optimization algorithm (WOA), sine-cosine algorithm (SCA), Harris' hawk optimization (HHO), and equilibrium optimizer (EO). The experimental results suggest that AOA is a high-performance optimization tool with respect to convergence speed and exploration-exploitation balance, as it is effectively applicable for solving complex problems. The source code is currently available for public from: https://www.mathworks.com/matlabcentral/fileexchange/79822-archimedes-optimization-algorithm [ABSTRACT FROM AUTHOR]

Summary: In this paper, a new population‐based meta‐heuristic algorithm called Social Optimization Algorithm (SOA) is proposed. This algorithm has been inspired by the social behavior of human‐beings, and mimics an ideal justly‐established society where the two principles of justice, that is, equality of opportunity and community principles are meticulously followed. Reaching goals in such a society is optimally achieved. Although a real fully‐just society does not exist in the real world, we implement the principles in such ideal societies through simulations to introduce our new optimization algorithm. The advantage of the proposed method is that it does not have any algorithm‐specified parameters to be set. This is a great merit compared to similar meta‐heuristics. The algorithm has been simulated on 23 well‐known test functions, and the results have been compared with those produced by Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA) and Differential Evolution (DE). In 57% of the 23 cases SOA yields superior results. The algorithm has also been tested on five other benchmark functions to be compared with Teaching‐Learning‐Based Optimization (TLBO) and Artificial Bee Colony (ABC).The results provide evidence for efficient performance with superior solution quality of the proposed algorithm in solving benchmark problems. To further illustrate the effectiveness of the proposed algorithm, it has been tested on the Economic Dispatch problem as a real‐world constrained optimization problem. The results have been compared with Biogeography‐Based Optimizer (BBO), Harmony Search (HS) and PSO. The results show that the SOA is able to produce very competitive results compared to other well‐known meta‐heuristic algorithms. Meta‐heuristic, SOA, Economic Dispatch. [ABSTRACT FROM AUTHOR]

Conventional optimization methods are not efficient enough to solve many of the naturally complicated optimization problems. Thus, inspired by nature, metaheuristic algorithms can be utilized as a new kind of problem solvers in solution to these types of optimization problems. In this paper, an optimization algorithm is proposed which is capable of finding the expected quality of different locations and also tuning its exploration-exploitation dilemma to the location of an individual. A novel particle swarm optimization algorithm is presented which implements the conditioning learning behavior so that the particles are led to perform a natural conditioning behavior on an unconditioned motive. In the problem space, particles are classified into several categories so that if a particle lies within a low diversity category, it would have a tendency to move towards its best personal experience. But, if the particle's category is with high diversity, it would have the tendency to move towards the global optimum of that category. The idea of the birds' sensitivity to its flying space is also utilized to increase the particles' speed in undesired spaces in order to leave those spaces as soon as possible. However, in desirable spaces, the particles' velocity is reduced to provide a situation in which the particles have more time to explore their environment. In the proposed algorithm, the birds' instinctive behavior is implemented to construct an initial population randomly or chaotically. Experiments provided to compare the proposed algorithm with the state-of-the-art methods show that our optimization algorithm is one of the most efficient and appropriate ones to solve the static optimization problems. [ABSTRACT FROM AUTHOR]

Permutation Flow-Shop Scheduling Problem (PFSP) which exists in many manufacturing systems is a classic combinatorial optimization problem. Studies have shown that the PFSP including more than three machines belongs to the NP-hard problems and is difficult to solve. Based on a new bio-inspired algorithm { Artificial Buttery Optimization (ABO) algorithm, this paper presents a Discrete Artificial Buttery Optimization (DABO) algorithm to find the permutation that gives the smallest completion time or the smallest total ow time. The performance of the proposed algorithm is tested on well-known benchmark suites of Car, Reeves and Taillard. The experimental results show that the proposed algorithm is able to provide very promising and competitive results on most benchmark functions. The DABO algorithm is then employed for one production optimization problem. [ABSTRACT FROM AUTHOR]

This paper presents the solution to mitigate the total harmonic distortion (THD) in multilevel inverters (MLIs) using novel improved whale optimization algorithm (IWOA). The IWOA falls under the category of swarm-based nature inspired optimization algorithms. It uses a novel diffusion process using a random walk technique and utilizes an additional ranking system to estimate the optimum solution to minimize THD. Moreover, THD minimization is further accomplished through nine various meta-heuristic algorithms for investigation and comparative analysis. The selected algorithms along with the proposed IWOA are rigorously tested on single phase 5 and 7 level cascaded H-Bridge MLIs for various performance parameters such as consistency, computational efficiency and speed of convergence. It is found that the proposed algorithm outperforms the nine algorithms and is efficient for THD minimization for modulation index (MI) in the range of 0–1. The results are analyzed and reported after thorough verification using MATLAB simulation. It presents a novel switching technique through improved whale optimization algorithm (IWOA) to optimize the switching angles of a single phase 5 and 7 level multilevel inverters (MLIs) in order to minimize the total harmonic distortion (THD). IWOA incorporates a novel diffusion process and two new position updating techniques The proposed IWOA provides better computational efficiency, improved consistency and faster convergence compared to the older whale optimization algorithm (WOA) with minimal tuning of the algorithm's parameters. The proposed IWOA is compared alongside various meta-heuristics like genetic algorithm (GA), particle swarm optimization (PSO), grey wolf optimizer (GWO), krill herd (KH), artificial electric field algorithm (AEFA), sun flower optimizer (SFO), galactic swarm optimization (GSO), fruit fly optimization algorithm (FOA) and whale optimization algorithm (WOA) for optimal THD minimization. [ABSTRACT FROM AUTHOR]

Among population-based optimization algorithms guided by meta-heuristics, Particle Swarm Optimization (PSO) has gained significant popularity in the past two decades, particularly due to its ease of implementation and fast convergence capabilities. This paper seeks to translate the beneficial features of PSO from solving typical continuous single-objective problems to solving multi-objective mixed-discrete problems, which is relatively a new ground for PSO application. The original Mixed-Discrete PSO (MDPSO) algorithm, which included an exclusive diversity preservation technique to significantly mitigate premature particle clustering, has been shown to be a powerful single-objective solver for highly constrained MINLP problems. This papers makes fundamental advancements to MDPSO, enabling it to solve complex multi-objective problems with mixed-discrete design variables. Specifically, in the velocity update equation for any particle, the explorative term is modified to point towards a stochastically selected non-dominated solution at that iteration − thereby adopting the concept of multi-leader swarms. The fractional domain in the diversity preservation technique, which was previously defined in terms of the best global particle, is now formulated as a function of the extreme members in the set of intermediate Pareto optimal solutions. With this advancement, diversity preservation not only mitigates premature particle stagnation, but also promotes more uniform coverage of the Pareto frontier. The multi-objective MDPSO algorithm is tested using a set of benchmark problems and a wind farm layout optimization problem. To illustrate the competitive benefits of the new MO-MDPSO algorithm, the results are compared with those given by other popular multi-objective solvers such as NSGA-II and SPEA. [ABSTRACT FROM AUTHOR]

Butterfly optimization algorithm (BOA) is a novel nature-inspired metaheuristics algorithm proposed in recent years. The basic BOA is slow convergence, low accuracy and easy to fall into local optimum. To solve the above problem of basic BOA, this paper proposed an adaptive butterfly optimization algorithm based on mutation strategies ( ABO A-MS) . Firstly, it introduced the strategy of adjust the conversion probability dynamically, which effectively balanced the ability of the global exploration and local search, by means of dynamically adjusting the switching probability of the change information of iteration times and individual fitness. Secondly, it introduced the adaptive inertia weight strategy and local mutation strategy. It applied the inertia weight value and chaotic memory weight factor, which further improved the diversity of this algorithm, and effectively avoided its precocious convergence, as well as accelerated its convergence speed and solving accuracy. In order to verify the optimization performance of the modified algorithm, it carried out the simulation experiments among the modified algorithm, the basic BOA algorithm, the particle swarm optimization algorithm, the salp swarm algorithm, the gray wolf optimization and the others. Simulation results illustrate that the modified algorithm has excellent performance of fast convergence speed, high optimization accuracy and strong stability. [ABSTRACT FROM AUTHOR]

In this paper, a novel fast heuristic optimization algorithm has been proposed that effectively boosts the exploration and exploitation process during the search of a global optimum in the search region. In this proposed algorithm, a particle position dependent stochastic variable is used to control the exploration and exploitation process. It is also used to reduce the computational time requirement. The search region shrinks efficiently in a continuous manner during successive iterations. The performance of the algorithm has been validated by comparing the simulation results of the particle swarm optimization (PSO), quantum particle swarm optimization (QPSO) and firefly algorithm (FFA) using some well-known benchmark functions. The proposed algorithm reduces the computational time around 44.2% compared to others in finding the global optimum point. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the optimal design of a grid-dependent and off-grid hybrid renewable energy system (RES). This system consists of Photovoltaic (PV), Wind Turbine (WT) as well as Fuel Cell (FC) with hydrogen gas tank for storing the energy in the chemical form. The optimal components sizes of the proposed hybrid generating system are achieved using a novel metaheuristic optimization technique. This optimization technique, called Improved Artificial Ecosystem Optimization (IAEO), is proposed for enhancing the performance of the conventional Artificial Ecosystem Optimization (AEO) algorithm. The IAEO improves the convergence trends of the original AEO, gives the best minimum objective function, reaches the optimal solution after a few iterations numbers as well as reduces the falling into the local optima. The proposed IAEO algorithm for solving the multiobjective optimization problem of minimizing the Cost of Energy (COE), the reliability index presented by the Loss of Power Supply Probability (LPSP), and excess energy under the constraints are considered. The hybrid system is suggested to be located in Ataka region, Suez Gulf (latitude 30.0, longitude 32.5), Egypt, and the whole lifetime of the suggested case study is 25 years. To ensure the accurateness, stability, and robustness of the proposed optimization algorithm, it is examined on six different configurations, representing on-grid and off-grid hybrid RES. For all the studied cases the proposed IAEO algorithm outperforms the original AEO and generates the minimum value of the fitness function in less execution time. Furthermore, comprehensive statistical measurements are demonstrated to prove the effectiveness of the proposed algorithm. Also, the results obtained by the conventional AEO and IAEO are compared with those obtained by several well-known optimization algorithms, Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), and Grey Wolf Optimizer (GWO). Based on the obtained simulation results, the proposed IAEO has the best performance among other algorithms and it has successfully positioned itself as a competitor to novel algorithms for tackling the most complicated engineering problems. [ABSTRACT FROM AUTHOR]

Metaheuristic optimization methods have been implemented for solving several problems. However, when it is required to implement those algorithms in hardware to run online, there is not enough information. This paper describes how could be programmed and implemented those optimization algorithms. Moreover, a complete evaluation is shown as well as a comparative study regarding the most important metaheuristic optimization algorithms. Thus, this paper presents a comparison between five optimization algorithms implemented into a cRIO field-programmable gate array (LabVIEW FPGA) NI-9030 of National Instruments T M (NI). The algorithms implemented were particle swarm optimization (PSO), bat algorithm (BA), grey wolf optimizer (GWO), earthquake algorithm (EA), and Nelder–Mead algorithm (NM). To analyze hardware device utilization and execution time by each algorithm, synthesis results were presented. In addition, a set of ten benchmark functions was selected to compare performance between algorithms. Results show the feasibility of this approach for NI FPGA hardware. From device utilization results, GWO presents the lowest placed usage (29%) while NM shows the fastest execution time (0.683 ms). Nevertheless, PSO, GWO and EA show better performance between benchmark functions due their exploration characteristics which make possible to find a better solution. [Display omitted] • Information about optimization implementation in hardware to run online is not enough • A hardware implementation of five optimization algorithms is described. • Five optimization algorithms were implemented into a cRIO LabVIEW FPGA. • Complete and comparative studies of those optimization algorithms are presented. • EA and GWO optimization algorithms are first time implemented into a FPGA. [ABSTRACT FROM AUTHOR]

Metaheuristic optimization is considered one of the most efficient and powerful techniques of recent decades as it can deal effectively with complex optimization problems. The performance of the optimization technique relies on two main components: exploration and exploitation. Unfortunately, the performance is limited by a weakness in one of the components. This study aims to tackle the issue with the exploration of the existing jellyfish search optimizer (JSO) by introducing a hybrid jellyfish search and particle swarm optimization (HJSPSO). HJSPSO is mainly based on a JSO structure, but the following ocean current movement operator is replaced with PSO to benefit from its exploration capability. The search process alternates between PSO and JSO operators through a time control mechanism. Furthermore, nonlinear and time-varying inertia weight, cognitive, and social coefficients are added to the PSO and JSO operators to balance between exploration and exploitation. Sixty benchmark test functions, including 10 CEC-C06 2019 large-scale benchmark test functions with various dimensions, are used to showcase the optimization performance. Then, the traveling salesman problem (TSP) is used to validate the performance of HJSPSO for a nonconvex optimization problem. Results demonstrate that compared to existing JSO and PSO techniques, HJSPSO contributes in terms of exploration and exploitation improvements, where it outperforms other well-known metaheuristic optimization techniques that include a hybrid algorithm. In this case, HJSPSO secures the first rank in classical and large-scale benchmark test functions by achieving the highest hit rates of 64% and 30%, respectively. Moreover, HJSPSO demonstrates good applicability in solving an exemplar TSP after attaining the shortest distance with the lowest mean and best fitness at 37.87 and 36.12, respectively. Overall, HJSPSO shows superior performance in solving most benchmark test functions compared to other optimization techniques, including JSO and PSO. As a conclusion, HJSPSO is a robust technique that can be applied to solve most optimization problems with a promising solution. [ABSTRACT FROM AUTHOR]

Moth-flame optimization (MFO) is a prominent problem solver with a simple structure that is widely used to solve different optimization problems. However, MFO and its variants inherently suffer from poor population diversity, leading to premature convergence to local optima and losses in the quality of its solutions. To overcome these limitations, an enhanced moth-flame optimization algorithm named MFO-SFR was developed to solve global optimization problems. The MFO-SFR algorithm introduces an effective stagnation finding and replacing (SFR) strategy to effectively maintain population diversity throughout the optimization process. The SFR strategy can find stagnant solutions using a distance-based technique and replaces them with a selected solution from the archive constructed from the previous solutions. The effectiveness of the proposed MFO-SFR algorithm was extensively assessed in 30 and 50 dimensions using the CEC 2018 benchmark functions, which simulated unimodal, multimodal, hybrid, and composition problems. Then, the obtained results were compared with two sets of competitors. In the first comparative set, the MFO algorithm and its well-known variants, specifically LMFO, WCMFO, CMFO, ODSFMFO, SMFO, and WMFO, were considered. Five state-of-the-art metaheuristic algorithms, including PSO, KH, GWO, CSA, and HOA, were considered in the second comparative set. The results were then statistically analyzed through the Friedman test. Ultimately, the capacity of the proposed algorithm to solve mechanical engineering problems was evaluated with two problems from the latest CEC 2020 test-suite. The experimental results and statistical analysis confirmed that the proposed MFO-SFR algorithm was superior to the MFO variants and state-of-the-art metaheuristic algorithms for solving complex global optimization problems, with 91.38% effectiveness. [ABSTRACT FROM AUTHOR]

Agent-based algorithms, based on the collective behavior of natural social groups, exploit innate swarm intelligence to produce metaheuristic methodologies to explore optimal solutions for diverse processes in systems engineering and other sciences. Especially for complex problems, the processing time, and the chance to achieve a local optimal solution, are drawbacks of these algorithms, and to date, none has proved its superiority. In this paper, an improved swarm optimization technique, named Grand Tour Algorithm (GTA), based on the behavior of a peloton of cyclists, which embodies relevant physical concepts, is introduced and applied to fourteen benchmarking optimization problems to evaluate its performance in comparison to four other popular classical optimization metaheuristic algorithms. These problems are tackled initially, for comparison purposes, with 1000 variables. Then, they are confronted with up to 20,000 variables, a really large number, inspired in the human genome. The obtained results show that GTA clearly outperforms the other algorithms. To strengthen GTA's value, various sensitivity analyses are performed to verify the minimal influence of the initial parameters on efficiency. It is demonstrated that the GTA fulfils the fundamental requirements of an optimization algorithm such as ease of implementation, speed of convergence, and reliability. Since optimization permeates modeling and simulation, we finally propose that GTA will be appealing for the agent-based community, and of great help for a wide variety of agent-based applications. [ABSTRACT FROM AUTHOR]

• The path planning and path smoothing are combined through the high-order continuous Bezier curve. • An improved QPSO that incorporates two search mechanisms from bald eagle search (BES) is proposed. • The concept of semi-randomization is infused in some of our modifications in BES-MQPSO. • Enhanced intensive exploitation is performed by incorporating BES search and swoop mechanisms. • It may be the first time to apply BES tricks to help to enhance QPSO and into the path planning field. The design of path planning algorithms to obtain a smooth path that satisfies constraints such as obstacle avoidance, minimized cost, and dynamic feasibility for mobile robots in the workspace is a hot issue in the field of intelligent robotics. The metaheuristic optimization algorithm, which aims to solve path planning problems, is more efficient compared to traditional algorithms. In the current metaheuristic approaches, avoiding local minimum traps is the core goal of the scheme. In this paper, with the benefit of a high-order continuous Bezier curve, we construct an optimization problem of smooth path planning based on the length and the requirement of collision-free safety as constraints of the robot's expected path. Then, the smooth path planning problem is transformed into an optimization problem that searches the locations of control nodes of the Bezier curve. We solve the constructed path planning problem with our modified quantum particle swarm optimization algorithm (QPSO). Specifically, to overcome the shortcomings of the current PSO and QPSO-based particle swarm algorithms like prematurity, unbalanced global search, local exploitation, and local minima, we implement an improved QPSO algorithm denoted as BES-MQPSO that incorporates the search and swoop mechanisms from bald eagle search (BES) strategies. The method consists of four improved modifications: an improved local attractor, improved feature length, BES-enhanced intensive exploitation Strategy, and random variation mechanism. Our proposed BES-MQPSO with these four modifications is tested with benchmark functions and then applied to the smooth path planning problem. Finally, the superiority of BES-MQPSO is confirmed, and the ideal smooth path of the mobile robot is successfully planned. The planned path length is reduced by 4.45% and 2.82% compared to e-QPSO and SDEQPSO respectively, while the standard deviation is reduced by 67.46% and 69.82%, respectively. [ABSTRACT FROM AUTHOR]

Meta-heuristics are powerful tools for solving optimization problems whose structural properties are unknown or cannot be exploited algorithmically. We propose such a meta-heuristic for a large class of optimization problems over discrete domains based on the particle swarm optimization (PSO) paradigm. We provide a comprehensive formal analysis of the performance of this algorithm on certain "easy" reference problems in a black-box setting, namely the sorting problem and the problem OneMax. In our analysis we use a Markov model of the proposed algorithm to obtain upper and lower bounds on its expected optimization time. Our bounds are essentially tight with respect to the Markov model. We show that for a suitable choice of algorithm parameters the expected optimization time is comparable to that of known algorithms and, furthermore, for other parameter regimes, the algorithm behaves less greedy and more explorative, which can be desirable in practice in order to escape local optima. Our analysis provides a precise insight on the tradeoff between optimization time and exploration. To obtain our results we introduce the notion of indistinguishability of states of a Markov chain and provide bounds on the solution of a recurrence equation with non-constant coefficients by integration. [ABSTRACT FROM AUTHOR]

Summary: Scientific and technological advancements lead to the continuous generation of a large amount of data. These datasets are analyzed computationally to reveal patterns and trends. While the presence of noisy and irrelevant features or attributes in these datasets is unavoidable, they negatively impact the performance of classification techniques. Feature selection is a method to pre‐process these datasets by selecting the most informative features while concurrently improving the classification accuracy. Recently, several metaheuristic algorithms were employed in this feature selection process, including particle swarm optimization (PSO). PSO is prominent in the field of feature selection due to its simplicity and global search abilities. However, it may get stuck in local optima. To solve this problem, a new update mechanism in PSO is proposed and the PSO is hybridized with a local search method. To evaluate the performance of the proposed algorithm, benchmark datasets from the University of California in Irvine (UCI) repository were utilized, the k‐nearest neighbor as the classifier. Results show that the proposed feature selection technique outperforms other optimization algorithms on these feature selection problems. [ABSTRACT FROM AUTHOR]

Rao algorithms are metaheuristic algorithms that are based on population and do not have metaphors. These algorithms are extremely simple and do not require the use of any parameters that are dependent on the problem. Although these algorithms have some other benefits to, they are vulnerable of being trapped in local optima. The present work proposes Enhanced Rao algorithms denoted by ERao as a means of alleviating this drawback. In the ERao algorithms, the modified version of the statistically regenerated mechanism is added. Additionally, the mechanism that sticks the candidate solution to the border of the search space is modified. The efficiency of the ERao algorithms is tested on three structural design optimization problems with probabilistic and deterministic constraints. The optimization results are compared to those of the Rao algorithms and some other state-of-art optimization methods. The results show that the proposed optimization method can be an effective tool for solving structural design problems with probabilistic and deterministic constraints. [ABSTRACT FROM AUTHOR]

• We integrate a PSO with a NN and ERNN to form PSO-NN and PSO-ERNN metaheuristic models. • The PSO trains the NN and ERNN to achieve faster convergence and avoid local minima. • The PSO-NN and PSO-ERNN models are evaluated using ten benchmark classification datasets. • The accuracy and computational efficiency of the models are improved. • The PSO-ERNN model outperforms the PSO-NN model for most of the tested datasets. There are several types of neural networks (NNs) that are widely used for data classification tasks. The supervised learning NN is an advanced network with a training algorithm for setting the weights and biases of the network in its training phase. However, traditional training algorithms such as backpropagation have some drawbacks, such as slow convergence speed and falling into local minima, which reduces the performance of the classifier. Therefore, different nature-inspired metaheuristic algorithms are integrated with the NN training algorithms to provide derivative-free solutions for complex classification problems. Consequently, this paper proposes the integration of a particle swarm optimization (PSO) algorithm with an improved Elman recurrent neural network (ERNN) to form a PSO-ERNN metaheuristic model. The key contribution of this study is the development of a new dimensional equation for ERNN architecture and the integration of PSO in ERNN learning to produce the PSO-ERNN model. The PSO is constructed to train the NN and ERNN models to achieve a fast convergence rate and avoid local minima problems. The PSO-ERNN model is validated by comparing it against the standard PSO-NN metaheuristic model and similar models from the literature. The PSO-NN and PSO-ERNN models are tested and evaluated using ten benchmark classification problems of breast cancer, heart, hepatitis, liver, wine, iris, lung cancer, yeast, Pima Indians diabetes, and ionosphere datasets. In the training phase, the results show that the PSO-ERNN model performs better than the PSO-NN model when the training set has a bigger size of samples. In the testing phase, the PSO-ERNN model outperforms the PSO-NN model for all the tested datasets except the lung cancer and yeast datasets, in which the accuracy percentage slightly decreases. In the validation phase, the PSO-ERNN model shows better performance quality in terms of accuracy percentage in six of the tested datasets. The average percentage of the training, testing, and validation accumulation show that the PSO-NN performs better than the PSO-ERNN in the lung cancer (87.27, 83.32), and heart (73.56, 70.64) datasets. On the other hand, the PSO-ERNN performs better than the PSO-NN in the iris (88.18, 86.74), hepatitis (88.60, 87.93), wine (89.16, 86.08), liver (73.56, 70.64), ionosphere (83.98, 78.94), and breast cancer (94.84, 91.17). PSO-NN and PSO-ERNN produce the same average results in the Pima Indians diabetes (84.00, 84.00) and yeast (91.31, 91.30) dataset. These results show clearly that the PSO-ERNN generally outperforms the PSO-NN when considering the overall results of the ten datasets. Nevertheless, the combinations of the PSO-NN and PSO-ERNN are proven to represent consistent and robust classification methods. The computational efficiencies of the training processes in both the PSO-NN and PSO-ERNN models are highly improved when coupled with the PSO. [ABSTRACT FROM AUTHOR]

This paper considers a variant of the classical capacitated vehicle routing problem called clustered vehicle routing problem (CluVRP). In CluVRP, customers are grouped into different clusters. A vehicle visiting a cluster cannot leave the cluster until all customers in the same cluster have been served. Each cluster and customer have to be served only once. A new hybrid metaheuristic, combining the particle swarm optimization (PSO) and variable neighborhood search (VNS) for the specific problem, is proposed to solve the CluVRP. In the hybrid PSO, the basic PSO principle ensures the solution diversity and VNS ensures solution intensity to bring the solution to the local optima. Extensive computational experiments have been performed on numerous benchmark instances with various sizes obtained from the CluVRP literature to evaluate the performance of the proposed hybrid PSO. The obtained results of the proposed algorithm are compared with the results found in the literature to validate the effectiveness of the proposed hybrid PSO. The proposed algorithm is proven to be superior to the state-of-the-art algorithms on the CluVRP. The proposed algorithm obtains 138 new best-known solutions among the 293 benchmark instances. • A variant of the CVRP called clustered vehicle routing problem is considered. • A new hybrid PSO metaheuristic is proposed. • Computational experiments are performed on benchmark instances with various sizes. • The new best-known solutions for a total of 138 instances are obtained. • The proposed algorithm shows the great implication in many practical scenarios. [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]

The marine predator algorithm is a new nature-inspired metaheuristic algorithm that mimics biological interaction between marine predators and prey. It has been also stated from the literature that this algorithm can solve many real-world optimization problems which made it a new popular optimization technique for the researchers. However, there is still a deficiency in the marine predator algorithm such as the inability to produce a diverse initial population with high productivity, lack of quick escaping of the local optimization, and lack of widely and broadly exploration of the search space. In the present study, a developed version of this algorithm is proposed based on the opposition-based learning method, chaos map, self-adaptive of population, and switching between exploration and exploitation phases. The simulations are performed using MATLAB environment on standard test functions including CEC-06 2019 tests and a real-world optimization problem based on PID control applied to a DC motor to evaluate the performance of the suggested algorithm. The simulation results are compared with the original marine predator algorithm and five state-of-the-art optimization algorithms namely Particle Swarm Optimization, Grasshopper Optimization Algorithm, JAYA Algorithm, Equilibrium optimizer Algorithm, Whale Optimization Algorithm, Differential Search Algorithm, and League Championship Algorithm. Eventually, the simulation results proved that the suggested algorithm has better results compared with other algorithms for the studied case studies. [ABSTRACT FROM AUTHOR]

Wolf Pack Algorithm (WPA) is a swarm intelligence algorithm that simulates the food searching process of wolves. It is widely used in various engineering optimization problems due to its global convergence and computational robustness. However, the algorithm has some weaknesses such as low convergence speed and easily falling into local optimum. To tackle the problems, we introduce an improved approach called OGL-WPA in this work, based on the employments of Opposition-based learning and Genetic algorithm with Levy's flight. Specifically, in OGL-WPA, the population of wolves is initialized by opposition-based learning to maintain the diversity of the initial population during global search. Meanwhile, the leader wolf is selected by genetic algorithm to avoid falling into local optimum and the round-up behavior is optimized by Levy's flight to coordinate the global exploration and local development capabilities. We present the detailed design of our algorithm and compare it with some other nature-inspired metaheuristic algorithms using various classical test functions. The experimental results show that the proposed algorithm has better global and local search capability, especially in the presence of multi-peak and high-dimensional functions. [ABSTRACT FROM AUTHOR]

The maximum power value that can be obtained from photovoltaic systems can change continuously due to environmental conditions such as temperature, sunlight and partial shading. Direct current-direct current (DC-DC) converters and maximum power point tracking (MPPT) algorithms are required, especially in cases of partial shading, in order for the photovoltaic systems to operate at the maximum power point, that is, to draw the maximum possible power value from the system. In this study, simulation studies has been carried out for two different partially shaded scenarios using the boost-type DC-DC converter and MPPT algorithm in the PV array consisting of 3 panels connected in series. In the simulation studies, the output powers obtained by the application of particle swarm optimization, cuckoo optimization, bat optimization and firefly optimization techniques as MPPT algorithm has been compared. In the scenarios examined, the firefly optimization algorithm reached the maximum power point faster, and it has been observed that the firefly optimization method obtained the highest average power at the end of the simulation periods. [ABSTRACT FROM AUTHOR]

One of the most important methods of image processing is image thresholding, which is based on image histogram analysis. These methods analyze the image histogram diagram and try to present optimal values for the image thresholds so that the image regions can be distinguished by these thresholds. Thresholding is a popular method in image processing and is used in most research related to image segmentation due to its accuracy and efficiency. Multi-level thresholding, such as the Otsu method, is one of the most common methods of thresholding image processing. These methods have high computational complexity despite their accuracy and efficiency. When the number of thresholds used increases, these methods lose their efficiency due to increased complexity and execution time. One of the ways to find thresholds in the Otsu threshold method is to use metaheuristic algorithms such as the Black Widow Spider Optimization Algorithm. These algorithms can find the appropriate thresholds for the image at the logical time. In the proposed method, each threshold is a component or one dimension of a solution of the Black Widow Spider Optimization Algorithm, and an attempt is made to calculate the optimal threshold value without high complexity by this algorithm. Experiments on several standard images show that the proposed algorithm finds better thresholds than the particle swarm optimization algorithm, the firefly algorithm, the genetic algorithm, and the gray wolf optimization algorithm. The analysis shows that the proposed method in the PSNR index has a better value in 83.33% of the experiments than other algorithms and also in 80% of the experiments the proposed method has a better SSIM index than these methods. Analysis of the proposed algorithm on several pertussis images also shows that the proposed method has a good ability to threshold medical images such as brain tumors and optic disc detection in human retinal images. [ABSTRACT FROM AUTHOR]

Swarm algorithms belong to the class of population metaheuristic optimization methods. Despite the use of various metaphors, most swarm algorithms have similar structures, where one can distinguish common components such as the decision population initialization, decision diversification, and decision intensification. Based on the concept of generality, an analysis of key approaches to, methods for, and ways of increasing the efficiency of swarm optimization algorithms was carried out. In the survey, swarm optimization algorithms are viewed as a set of operators without a detailed discussion of each algorithm. The main focus is on the analysis of the key components of the algorithms. The main idea behind efficiency improvement is to maintain a balance between diversification and intensification. In this context, we consider mechanisms for supporting population diversity, methods for tuning and adjusting the swarm algorithm parameters, and approaches to hybridization of algorithms. We also indicate several open problems related to the topic of the survey. [ABSTRACT FROM AUTHOR]