.png){kind=icon}
返回
Promising boundaries explore and resource allocation evolutionary algorithm for constrained multiobjective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2024.101819.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Constrained multiobjective optimization problems (CMOPs) typically present numerous local optima, which can be deceptive. Current constrained multiobjective algorithms (CMOEAs) encounter challenges in maintaining diversity and escaping these local optima because of the single function of the population in the same spacetime. Because they cannot keep exploring diversity and cannot balance their exploration focus. To this end, a dual-stage and dual-population algorithm named BPRRA is proposed in this article. Specifically, BPRRA utilizes new techniques to explore promising boundaries and allocate computing resources. In the first stage, one of the populations evolves to explore one promising boundary by ignoring constraints, and the other population explores another promising boundary by considering constraints. In the second stage, the two populations explore different regions from different promising boundaries using the diversity archiving strategy. Moreover, a novel resource allocation strategy is designed to dynamically allocate limited computational resources based on the ratio of potential offspring. The experiments involve five test suites and nine real-world problems to validate the performance of the proposed method. The results demonstrate that BPRRA has superior performance and can better solve CMOPs.
Keyword:
Constrained multiobjective optimization
Constraint handling
Evolutionary algorithm
Resource allocation technology
期刊
IF:
8.5
论文数:
2.2K
被引数:
1.0W
机构
引用论文
Indicator-Based Evolutionary Algorithm for Solving Constrained Multiobjective Optimization Problems
.png){kind=icon}
PREAI
分享收藏
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective OptimizationPlatEMO: 用于进化多目标优化的MATLAB平台
OAAI
OAAI分享收藏Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
PREAI
分享收藏分享收藏
查看更多内容