LEM-PSO: a lightweight evolutionary-state-driven multiple information learning particle swarm optimization algorithm

Particle swarm optimization (PSO) has been widely used, in which each particle selects its learning sample relying on fitness information. Intuitively, fitness-based selection strategy is beneficial to optimization. However, excessive reliance on fitness information may cause premature convergence o...

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Published in:	Neural computing & applications Vol. 37; no. 27; pp. 22667 - 22688
Main Authors:	Yang, Xu, Li, Hongru
Format:	Journal Article
Language:	English
Published:	London Springer London 01.09.2025 Springer Nature B.V
Subjects:	Accuracy Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Efficiency Fitness Genetic algorithms Image Processing and Computer Vision Machine learning Neighborhoods Optimization Particle swarm optimization Probability and Statistics in Computer Science S.I.: Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications Special Issue on Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications Diversity information Lightweight Evolutionary state Particle swarm optimization
ISSN:	0941-0643, 1433-3058
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Abstract	Particle swarm optimization (PSO) has been widely used, in which each particle selects its learning sample relying on fitness information. Intuitively, fitness-based selection strategy is beneficial to optimization. However, excessive reliance on fitness information may cause premature convergence of the whole population. To solve the defects of PSO, a lightweight evolutionary-state-driven multiple information learning particle swarm optimization algorithm (LEM-PSO) is proposed. In the new proposed LEM-PSO, firstly, a lightweight multiple information learning strategy is proposed. Then, adaptive evolutionary-state adjustment mechanism is proposed. Finally, local optimum warning operation is used to help the stagnant population to jump from local optimums. The comprehensive performance of LEM-PSO is compared with seven popular PSO variants on CEC2013, CEC2017 and two engineering problems, and the results confirm the firmness of LEM-PSO.
AbstractList	Particle swarm optimization (PSO) has been widely used, in which each particle selects its learning sample relying on fitness information. Intuitively, fitness-based selection strategy is beneficial to optimization. However, excessive reliance on fitness information may cause premature convergence of the whole population. To solve the defects of PSO, a lightweight evolutionary-state-driven multiple information learning particle swarm optimization algorithm (LEM-PSO) is proposed. In the new proposed LEM-PSO, firstly, a lightweight multiple information learning strategy is proposed. Then, adaptive evolutionary-state adjustment mechanism is proposed. Finally, local optimum warning operation is used to help the stagnant population to jump from local optimums. The comprehensive performance of LEM-PSO is compared with seven popular PSO variants on CEC2013, CEC2017 and two engineering problems, and the results confirm the firmness of LEM-PSO.
Author	Yang, Xu Li, Hongru
Author_xml	– sequence: 1 givenname: Xu orcidid: 0000-0001-6853-9449 surname: Yang fullname: Yang, Xu organization: School of Electrical and Control Engineering, Shenyang Jianzhu University – sequence: 2 givenname: Hongru orcidid: 0000-0003-4700-962X surname: Li fullname: Li, Hongru email: lihongru@ise.neu.edu.cn organization: Information Science and Engineering, Northeastern University
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Snippet	Particle swarm optimization (PSO) has been widely used, in which each particle selects its learning sample relying on fitness information. Intuitively,...
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SubjectTerms	Accuracy Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Efficiency Fitness Genetic algorithms Image Processing and Computer Vision Machine learning Neighborhoods Optimization Particle swarm optimization Probability and Statistics in Computer Science S.I.: Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications Special Issue on Hybrid Approaches to Nature-inspired Optimization Algorithms and Their Applications
Title	LEM-PSO: a lightweight evolutionary-state-driven multiple information learning particle swarm optimization algorithm
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