Personalized Indicator Based Evolutionary Algorithm for Uncertain Constrained Many‐Objective Optimization Problem With Interval Functions

ABSTRACT In practical engineering problems, uncertainties due to prediction errors and fluctuations in equipment efficiency often lead to constrained many‐objective optimization problem with interval parameters (ICMaOPs). These problems pose significant challenges for evolutionary algorithms, partic...

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Veröffentlicht in:Concurrency and computation Jg. 37; H. 3
Hauptverfasser: Wen, Jie, Wang, Qian, Dong, Haozhe, Cui, Zhihua
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken, USA John Wiley & Sons, Inc 01.02.2025
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Schlagworte:
Algorithms
Balancing
constraint handling
Constraints
Convergence
Customization
Evolutionary algorithms
Feasibility studies
interval optimization
many‐objective optimization
Optimization
Parameter uncertainty
ISSN:1532-0626, 1532-0634
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Zusammenfassung:ABSTRACT In practical engineering problems, uncertainties due to prediction errors and fluctuations in equipment efficiency often lead to constrained many‐objective optimization problem with interval parameters (ICMaOPs). These problems pose significant challenges for evolutionary algorithms, particularly in balancing solution convergence, diversity, feasibility, and uncertainty. To address these challenges, a personalized indicator‐based evolutionary algorithm (PI‐ICMaOEA) specifically designed for ICMaOPs is proposed. The PI‐ICMaOEA integrates a comprehensive quality indicator that encapsulates convergence, diversity, uncertainty, and feasibility factors, converting multiple objectives in high‐dimensional search spaces into a single evaluative metric. Each factor's weight is personalized assigned based on individual performance, objective dimension, and the evolving conditions of the population. By prioritizing individuals with excellent indicator values for mating and environmental selection, PI‐ICMaOEA effectively enhances selection pressure in high‐dimensional spaces. Comparative simulations demonstrate that PI‐ICMaOEA is highly competitive, offering a robust solution for balancing convergence, diversity, uncertainty, and feasibility in ICMaOPs.
Bibliographie:Funding
This work was supported in part by the National Natural Science Foundation of China [Grant No. 61806138], in part by the China University Industry‐University‐Research Collaborative Innovation Fund (Future Network Innovation Research and Application Project) [Grant No. 2021FNA04014], and in part by the Key R&D Program of Shanxi Province under [Grant No. 202202020101012], and in part by the Program of Graduate Innovation Researching in Taiyuan University of Science and Technology under [Grant No. BY2023012].
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ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.8317