A Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) for Multimodal Functions

Shehadeh, Hisham A.; Mustafa, Hossam M. J.; Tubishat, Mohammad

doi:10.4018/ijamc.292507

Cited by 16 publications

(8 citation statements)

References 33 publications

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“…In this section, in order to verify the optimization performance of ADEDPM, we use 21 benchmark functions and 4 engineering example problems to compare them with five optimization algorithms in the past three years, which are AAGSA [27], DFPSO [28], HGASSO [29], HHO [30] and VAGWO [31], respectively.…”

Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

“…Its experimental results will be compared with the experimental results of the latest five optimization algorithms in the past three years. These algorithms are AAGSA [27], DFPSO [28], HGASSO [29], HHO [30], and VAGWO [31]. In the simulation experiment, the optimal value, the worst value, the average value and the standard deviation of the function measured by each optimization algorithm in 30 independent runs are used as the criteria for comparing the convergence accuracy and stability.…”

Section: Experimental Results Of Benchmark Test Functionsmentioning

confidence: 99%

“…The format of 21 benchmark functions is shown in the appendix. The experimental results of ADEDPM will be compared with the experimental results of five other optimization algorithms of the same type in the past three years, and these comparison algorithms are Adaptive Accelerated Gravitational Search Algorithm (AAGSA) [29], Dual Fitness Particle Swarm Optimization Algorithm (DFPSO) [30], Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) [31], Harris Hawks Optimizer (HHO) [32] and Velocity-Aided Grey Wolf Optimizer (VAGWO) [33]. The experimental results show that compared with the five latest optimization algorithms of the same type, the ADEDPM has strong competitiveness in each test indicators, they include the convergence accuracy, convergence speed, stability and rank sum test of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

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An adaptive differential evolution algorithm based on data preprocessing method to solve global optimization problem

Zhao

Wang

Zhang

2023

Preprint

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Differential evolution (DE) algorithm is a classical natural-inspired optimization algorithm, which has good optimization performance. However, with the deepening of research, some researchers found that the quality of the candidate solution of the population in the differential evolution algorithm is poor and its global search ability is not enough when solving the global optimization problem. Therefore, in order to solve the above problems, we propose an adaptive differential evolution algorithm based on data processing method (ADEDPM). In this paper, the data preprocessing method is implemented by k-means clustering algorithm, which is used to divide the initial population into multiple clusters according to the average value of fitness, and select candidate solutions in each cluster according to different proportions. This method improves the quality of candidate solutions of the population to a certain extent. In addition, in order to solve the problem of insufficient global search ability in differential evolution algorithm, we also proposed a new mutation strategy, which is called “DE/current-to-𝑝1 best&𝑝2 best”. This strategy guides the search direction of the differential evolution algorithm by selecting individuals with good fitness, so that its search range in the most promising candidate solution region, and indirectly increases the population diversity of the algorithm. Finally, we propose an adaptive parameter control method, which can effectively balance the relationship between the exploration process and the exploitation process to achieve better performance of the algorithm. In order to verify the effectiveness of the proposed algorithm, the ADEDPM is compared with five optimization algorithms of the same type in the past three years, which are AAGSA, DFPSO, HGASSO, HHO and VAGWO, respectively. In the simulation experiment, 21 benchmark test functions and 4 engineering example problems are used, and the convergence accuracy, convergence speed, stability and rank sum test of the algorithm are fully compared. The experimental results show that compared with the five latest optimization algorithms of the same type, the proposed algorithm has strong competitiveness in each test index.

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Section: Simulation Experiments and Results Analysismentioning

confidence: 99%

Section: Experimental Results Of Benchmark Test Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An adaptive differential evolution algorithm based on data preprocessing method to solve global optimization problem

Zhao

Wang

Zhang

2023

Preprint

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show abstract

“…Since certain algorithms contributes hugely to a good balance between exploration and exploitation, [29] recently proposed a Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) to optimize multimodal functions. The authors applied local search which is SSO first to select the global best solution and personal best solution before the selection, crossover and mutation is applied to jump out of the local minima easily.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Sperm Swarm Optimization and Genetic Algorithm for Unimodal and Multimodal Optimization Problems

et al. 2022

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A good exploration ability can ensure that the method jumps out of local optimum in multimodal problems and a good exploitation can ensure an algorithm converge faster to global optimum values. So, this paper proposes a new hybrid sperm swarm optimization and genetic algorithm to obtain global optimal solutions termed HSSOGA which is developed based on the concept of balancing the exploration and exploitation capability by merging Sperm Swarm Optimization (SSO), which has a fast convergence rate, and a Genetic Algorithm (GA) that can explore a search domain efficiently. To ensure that the proposed method delivers good performance, it is evaluated with 11 standard test function problems consisting of 5 unimodal and 6 multimodal functions. The proposed HSSOGA set is compared with conventional GA and SSO methods, as well as with several hybrid methods such as Hybrid Firefly and Particle Swarm Optimization (HFPSO), hybrid Simulated Annealing and Genetic Algorithm (SAGA), Hybrid Particle Swarm Optimization and Genetic Algorithm (HFPSO), hybrid Particle Swarm Optimization and Grey Wolf Optimization (PSOGWO), and closely related Hybrid Sperm Swarm Optimization and Gravitational Search Algorithm (HSSOGSA). The results are evaluated in terms of each method's best fitness, mean, standard deviation, and convergence rates. The numerical experiment results show that HSSOGA has better convergence towards the true global optimum values as compared to the conventional and existing hybrid methods in most unimodal and multimodal test function problems.

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“…Therefore, there is a need to utilize practical algorithms utilizing the heuristic information available in the problem model [6]. Accordingly, hybrid heuristic-metaheuristic techniques are recently more favored for solving complex optimization problems [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Designing a Renewable and Sustainable Phosphorus Fertilizer Supply Chain Network using an Ensemble Knowledge-based Heuristic-Metaheuristic Algorithm

Shokouhifar¹,

Sohrabi²,

Rabbani³

et al. 2022

Preprint

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Phosphorus (P) is the most important substance in inorganic fertilizers used in agriculture industry. In this study, a multi-product and multi-objective model is presented considering economic and environmental concerns to design a renewable and sustainable P-fertilizer supply chain management (PFSCM). To handle complexities of the proposed model, an ensemble knowledge-based three-stage heuristic-metaheuristic algorithm utilizing heuristic information available in the model, whale optimization algorithm, and variable neighborhood search (named H-WOA-VNS) is proposed. At first, a problem-dependent heuristic is designed to generate a set of near-optimal feasible solutions. These solutions are fed into a population-based whale optimization algorithm which benefits from both exploration and exploitation strategies. Finally, a single-solution metaheuristic based on variable neighborhood search is applied to further improve the quality of the solution using local search operators. The objective function of the algorithm is formulated as a weighted average function to minimize total economic cost, while increasing crop yield and P use efficiency. Experimental results over five synthetic datasets and a real case study of the P-fertilizer supply chain confirm the superiority of the proposed method against the state-of-the-art techniques. The results demonstrate that the proposed method performs well in optimizing both the economic cost and environmental issues.

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A Hybrid Genetic Algorithm and Sperm Swarm Optimization (HGASSO) for Multimodal Functions

Cited by 16 publications

References 33 publications

An adaptive differential evolution algorithm based on data preprocessing method to solve global optimization problem

An adaptive differential evolution algorithm based on data preprocessing method to solve global optimization problem

A Hybrid Sperm Swarm Optimization and Genetic Algorithm for Unimodal and Multimodal Optimization Problems

Designing a Renewable and Sustainable Phosphorus Fertilizer Supply Chain Network using an Ensemble Knowledge-based Heuristic-Metaheuristic Algorithm

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