.png){kind=icon}
.png){kind=icon}
Return
A Two-Archive Constrained Multi-Objective Optimization Algorithm Based on Two-Stage Weak Cooperation
.png){kind=icon}
PRE
AI
DOI：10.1002/cpe.70602.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Constrained multi-objective optimization problems widely exist in real-world applications, yet remain challenging due to the coexistence of multiple conflicting objectives and constraints. This study proposes a two-archive constrained multi-objective optimization algorithm with two-stage weak cooperation, named BWC-TAA. First, based on the two-archive framework, a weak cooperation interaction mechanism is designed, in which the CA and DA evolve independently. They only merge offspring during the update stage to select high-quality individuals, thereby preventing solutions from being confined to the parent population range. Second, a two-stage evolutionary strategy is introduced to dynamically adjust the optimization objectives, enabling CA and DA to adopt different strategies in different phases. Finally, BWC-TAA is evaluated on benchmark constrained problems against eight state-of-the-art algorithms. The experimental results demonstrate that BWC-TAA significantly outperforms the compared algorithms in terms of convergence and diversity indicators.
Keywords:
constrained and multi-objective optimization
evolutionary algorithm
two-archive
two-stage
weak-cooperation
Journal
C
IF:
1.5
Papers:
473
Citations:
0