.png){kind=icon}
返回
Composite indicator-guided infilling sampling for expensive multi-objective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2026.102312.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In computationally expensive multi-objective optimization, where the evaluation budget is severely limited, the selection of promising candidate solutions for costly fitness evaluations plays a critical role in accelerating convergence and enhancing algorithmic performance. Nevertheless, devising an optimization strategy that effectively balances convergence, diversity, and distribution remains a challenging task. To address this issue, this paper proposes a composite indicator-based evolutionary algorithm (CI-EMO) for expensive multi-objective optimization. During each generation of the optimization process, CI-EMO explores the solution space based on Gaussian Process model assisted NSGA-III, thereby generating a candidate population. Subsequently, a novel composite performance indicator is introduced to guide the selection of candidates for actual fitness evaluation. This indicator simultaneously accounts for convergence, diversity, and distribution, thereby enhancing the efficiency of identifying promising candidate solutions and significantly boosting algorithmic performance. The proposed composite indicator-based candidate selection strategy is straightforward to implement and computationally lightweight. Component analysis experiments validate the effectiveness of each constituent within the composite performance indicator. Comparative studies conducted on three benchmark test suites and real-world problems demonstrate that the proposed algorithm outperforms five state-of-the-art algorithms for expensive multi-objective optimization.
Keyword:
Expensive multi-objective optimization
Gaussian Process model
NSGA-III
Composite performance indicator
Infilling sampling
期刊
IF:
8.5
论文数:
2.2K
被引数:
1.0W
机构
引用论文
.png){kind=icon}
A Kriging-Assisted Two-Archive Evolutionary Algorithm for Expensive Many-Objective Optimization
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A Survey on Learnable Evolutionary Algorithms for Scalable Multiobjective Optimization可扩展多目标优化的可学习进化算法综述
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