Improved whale optimization algorithm with a stable solution preservation mechanism for multimodal multi-objective optimization

Multimodal multi-objective optimization problems (MMOPs) pose a challenge due to the need to identify multiple Pareto-optimal solution sets (PSs) distributed across both global and local regions. Although the whale optimization algorithm (WOA) has shown effectiveness in various optimization tasks, r...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Cluster computing Ročník 29; číslo 1; s. 35
Hlavní autoři: Sun, Yu, Chang, Yuqing, Hou, Shengle
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.02.2026
Springer Nature B.V
Témata:
Algorithms
Archives & records
Computer Communication Networks
Computer Science
Convergence
Decomposition
Effectiveness
Genetic algorithms
Identification methods
Multiple objective analysis
Neighborhoods
Operating Systems
Optimization algorithms
Pareto optimization
Pareto optimum
Processor Architectures
Self organizing maps
Cluster
Whale optimization algorithm
Self-organizing Map
Multimodal multi-objective optimization
ISSN:1386-7857, 1573-7543
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Multimodal multi-objective optimization problems (MMOPs) pose a challenge due to the need to identify multiple Pareto-optimal solution sets (PSs) distributed across both global and local regions. Although the whale optimization algorithm (WOA) has shown effectiveness in various optimization tasks, recent multi-objective WOA variants still suffer from premature convergence and inadequate preservation of solution diversity, limiting their ability to handle MMOPs. To address these issues, this paper proposes an improved whale optimization algorithm with a stable solution preservation mechanism (IWOA-SSPM). The proposed algorithm integrates a self-organizing map-based dual guidance mechanism to balance exploration and exploitation, and a stable solution preservation mechanism (SSPM) that combines a radius-based localized clearing method to identify and preserve multiple PSs with an intersection-based strategy to retain high-quality solutions across generations. Extensive experiments on benchmark MMOPs demonstrate that IWOA-SSPM achieves superior convergence and diversity compared to several competitive algorithms. Furthermore, a map-based optimization case study validates its practical effectiveness in solving complex real-world problems.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-025-05845-5