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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Neural computing & applications Ročník 37; číslo 27; s. 22667 - 22688
Hlavní autoři: Yang, Xu, Li, Hongru
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.09.2025
Springer Nature B.V
Témata:
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
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-025-11083-y