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Complex System Modeling and Simulation 2022, 2(4): 288-306 https://doi.org/10.23919/CSMS.2022.0018
Open Access | Issue | Published: 30 December 2022

Dual-Stage Hybrid Learning Particle Swarm Optimization Algorithm for Global Optimization Problems

Show Author's Information Wei Li1,  Yangtao Chen1,  Qian Cai1( ),  Cancan Wang1,  Ying Huang2,  Soroosh Mahmoodi3
School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
School of Mathematical and Computer Science, Gannan Normal University, Ganzhou 341000, China
Soroosh Khorshid Iranian Co., Abyek Industrial Zone, Qazvin 999067, Iran
Keywords:
particle swarm optimization, Manhattan distance, example learning, gaussian mutation, dual-stage, global optimization problem
Cite this article:
Li W, Chen Y, Cai Q, et al. Dual-Stage Hybrid Learning Particle Swarm Optimization Algorithm for Global Optimization Problems. Complex System Modeling and Simulation, 2022, 2(4): 288-306. https://doi.org/10.23919/CSMS.2022.0018
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Abstract Full text About this article

Particle swarm optimization (PSO) is a type of swarm intelligence algorithm that is frequently used to resolve specific global optimization problems due to its rapid convergence and ease of operation. However, PSO still has certain deficiencies, such as a poor trade-off between exploration and exploitation and premature convergence. Hence, this paper proposes a dual-stage hybrid learning particle swarm optimization (DHLPSO). In the algorithm, the iterative process is partitioned into two stages. The learning strategy used at each stage emphasizes exploration and exploitation, respectively. In the first stage, to increase population variety, a Manhattan distance based learning strategy is proposed. In this strategy, each particle chooses the furthest Manhattan distance particle and a better particle for learning. In the second stage, an excellent example learning strategy is adopted to perform local optimization operations on the population, in which each particle learns from the global optimal particle and a better particle. Utilizing the Gaussian mutation strategy, the algorithm’s searchability in particular multimodal functions is significantly enhanced. On benchmark functions from CEC 2013, DHLPSO is evaluated alongside other PSO variants already in existence. The comparison results clearly demonstrate that, compared to other cutting-edge PSO variations, DHLPSO implements highly competitive performance in handling global optimization problems.


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About this article

Dual-Stage Hybrid Learning Particle Swarm Optimization Algorithm for Global Optimization Problems

Show Author's information Wei Li1, Yangtao Chen1, Qian Cai1( ), Cancan Wang1, Ying Huang2, Soroosh Mahmoodi3
School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
School of Mathematical and Computer Science, Gannan Normal University, Ganzhou 341000, China
Soroosh Khorshid Iranian Co., Abyek Industrial Zone, Qazvin 999067, Iran

Abstract

Particle swarm optimization (PSO) is a type of swarm intelligence algorithm that is frequently used to resolve specific global optimization problems due to its rapid convergence and ease of operation. However, PSO still has certain deficiencies, such as a poor trade-off between exploration and exploitation and premature convergence. Hence, this paper proposes a dual-stage hybrid learning particle swarm optimization (DHLPSO). In the algorithm, the iterative process is partitioned into two stages. The learning strategy used at each stage emphasizes exploration and exploitation, respectively. In the first stage, to increase population variety, a Manhattan distance based learning strategy is proposed. In this strategy, each particle chooses the furthest Manhattan distance particle and a better particle for learning. In the second stage, an excellent example learning strategy is adopted to perform local optimization operations on the population, in which each particle learns from the global optimal particle and a better particle. Utilizing the Gaussian mutation strategy, the algorithm’s searchability in particular multimodal functions is significantly enhanced. On benchmark functions from CEC 2013, DHLPSO is evaluated alongside other PSO variants already in existence. The comparison results clearly demonstrate that, compared to other cutting-edge PSO variations, DHLPSO implements highly competitive performance in handling global optimization problems.

Keywords: particle swarm optimization, Manhattan distance, example learning, gaussian mutation, dual-stage, global optimization problem

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Received: 22 July 2022
Revised: 17 August 2022
Accepted: 07 September 2022
Published: 30 December 2022
Issue date: December 2022

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