.png){kind=icon}
返回
An Adaptive Two-Population Evolutionary Algorithm for Constrained Multi-Objective Optimization Problems
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3300590.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Striking a balance between objective optimization and constraint satisfaction is essential for solving constrained multi-objective optimization problems (CMOPs). Nevertheless, most existing evolutionary algorithms face significant challenges on CMOPs with intricate infeasible regions. To tackle these challenges, this paper proposes an adaptive two-population evolutionary algorithm, named ATEA, which dynamically exploits promising information under infeasible solutions to facilitate objective optimization and constraint satisfaction. Specifically, a two-population collaboration mechanism is designed to balance the unconstrained Pareto front search and constrained Pareto front search. Moreover, an adaptive constraint handling strategy is presented to reasonably deploy search resources. Furthermore, a promising infeasibility-based environmental selection and an elitist feasibility-based environmental selection are developed for the two populations to break through complex infeasible barriers and enhance selection pressure, respectively. Comparison experimental results of ATEA with five state-of-the-art algorithms on 33 benchmark test problems and 4 real-word CMOPs demonstrate that ATEA performs competitively with the chosen designs.
Keyword:
Constrained multi-objective optimization
evolutionary algorithm
two-population collaboration mechanism
multi-state
objective optimization
constraint satisfaction
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
Refining Estimates of Bird Collision and Electrocution Mortality at Power Lines in the United States.png)
PLoS ONE
OAAI
PLoS ONEIF0
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Preference-Inspired Co-Evolutionary Algorithms With Local PCA Oriented Goal Vectors for Many-Objective OptimizationIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容