.png){kind=icon}
返回
A constrained multi-objective evolutionary algorithm with tri-populations assisted by an additional objective function
.png){kind=icon}
PRE
AI
DOI：10.1007/s10586-024-04944-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Constrained multi-objective optimization problems are ubiquitous in real-world applications. Efficiently solving these problems necessitates a delicate equilibrium between feasible and infeasible solutions. In recent years, numerous constraint handling techniques have been proposed by scholars, primarily aimed at enhancing algorithmic solution performance. However, within complex feasible domains, an excessive reliance on methodologies that are solely geared towards addressing feasible or infeasible solutions has been shown to inhibit the efficiency of the algorithmic process of exploration. Seeking to leverage both solution types potential, this paper presents CMATA-an innovative tri-population constrained multi-objective evolutionary algorithm. The first two population evolve driven by feasibility; Specifically, the first population evolves based on the original problem, while the auxiliary second population optimizes M objective functions for the original problem, with constraint violations considered as an additional (M + 1) objective. A third population was evolved using an improved constraint relaxation technique. Throughout the evolutionary process, these three populations identify the constrained search area and collaborate to store viable options from the search process in an external archive. The efficacy of CMATA was tested against five advanced algorithms on 37 benchmark problems and a real-world problems. The results of these empirical tests confirm CMATA efficacy and competitive advantage in solving constrained multi-objective optimization problems.
Keyword:
Multi-objective optimization
Constrained optimization
Additional objective function
Constraint handling
Tri-population
期刊
C
IF:
4.1
论文数:
5.1K
被引数:
7.5K
机构
引用论文
Shape and Angle-of-Attack Measurements of Ram-Air Parachutes by Stereo Photogrammetry in the National Full-Scale Aerodynamics Complex国家全尺寸空气动力学实验室内利用立体摄影测量法对充气式降落伞的形状和攻角进行测量
PREAI
分享收藏USING WEIGHTED PERMUTATION SCORES TO DETECT DIFFERENTIAL GENE EXPRESSION WITH MICROARRAY DATA使用加权排列分数检测微阵列数据中的差异基因表达
PREAI
分享收藏Multi-objective differential evolution with dynamic hybrid constraint handling mechanism.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
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
查看更多内容