An improved composite particle swarm optimization algorithm for solving constrained optimization problems and its engineering applications

Ying Sun; Yuelin Gao; Ying Sun; Yuelin Gao

doi:10.3934/math.2024385

AIMS Mathematics

2024, Volume 9, Issue 4: 7917-7944. doi: 10.3934/math.2024385

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Research article

An improved composite particle swarm optimization algorithm for solving constrained optimization problems and its engineering applications

Ying Sun ^{1,2
,
,},
Yuelin Gao ^1,2

1.
Ningxia Collaborative Innovation Center of Scientific Computing and Intelligent Information Processing, North Minzu University, Yinchuan 750021, China
2.
School of Mathematics and Information Sciences, North Minzu University, Yinchuan 750021, China

Received: 08 January 2024 Revised: 21 February 2024 Accepted: 21 February 2024 Published: 26 February 2024
MSC : 90C26, 90C59

In the last few decades, the particle swarm optimization (PSO) algorithm has been demonstrated to be an effective approach for solving real-world optimization problems. To improve the effectiveness of the PSO algorithm in finding the global best solution for constrained optimization problems, we proposed an improved composite particle swarm optimization algorithm (ICPSO). Based on the optimization principles of the PSO algorithm, in the ICPSO algorithm, we constructed an evolutionary update mechanism for the personal best position population. This mechanism incorporated composite concepts, specifically the integration of the $ \varepsilon $-constraint, differential evolution (DE) strategy, and feasibility rule. This approach could effectively balance the objective function and constraints, and could improve the ability of local exploitation and global exploration. Experiments on the CEC2006 and CEC2017 benchmark functions and real-world constraint optimization problems from the CEC2020 dataset showed that the ICPSO algorithm could effectively solve complex constrained optimization problems.
- particle swarm optimization algorithm,
- constrained optimization problem,
- DE strategy,
- $ \varepsilon $-constraint,
- feasibility rule,
- engineering application
Citation: Ying Sun, Yuelin Gao. An improved composite particle swarm optimization algorithm for solving constrained optimization problems and its engineering applications[J]. AIMS Mathematics, 2024, 9(4): 7917-7944. doi: 10.3934/math.2024385

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Abstract

In the last few decades, the particle swarm optimization (PSO) algorithm has been demonstrated to be an effective approach for solving real-world optimization problems. To improve the effectiveness of the PSO algorithm in finding the global best solution for constrained optimization problems, we proposed an improved composite particle swarm optimization algorithm (ICPSO). Based on the optimization principles of the PSO algorithm, in the ICPSO algorithm, we constructed an evolutionary update mechanism for the personal best position population. This mechanism incorporated composite concepts, specifically the integration of the $ \varepsilon $-constraint, differential evolution (DE) strategy, and feasibility rule. This approach could effectively balance the objective function and constraints, and could improve the ability of local exploitation and global exploration. Experiments on the CEC2006 and CEC2017 benchmark functions and real-world constraint optimization problems from the CEC2020 dataset showed that the ICPSO algorithm could effectively solve complex constrained optimization problems.

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