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Composite Indicator-Guided Infilling Sampling for Expensive Multi-Objective Optimization by Zhen, Huixiang; Li, Xiaotong; Gong, Wenyin; Wang, Ling; Hu, Xiangyun is a scholarly article available to read on EtoBox.

What is Composite Indicator-Guided Infilling Sampling for Expensive Multi-Objective Optimization about?

In expensive multi-objective optimization, where the evaluation budget is strictly limited, selecting promising candidate solutions for expensive fitness evaluations is critical for accelerating convergence and improving algorithmic performance. However, designing an optimization strategy that effectively balances convergence, diversity, and distribution remains a challenge. To tackle this issue, we propose a composite indicator-based evolutionary algorithm (CI-EMO) for expensive multi-objective optimization. In each generation of the optimization process, CI-EMO first employs NSGA-III to explore the solution space based on fitness values predicted by surrogate models, generating a candidate population. Subsequently, we design a novel composite performance indicator to guide the selection of candidates for real fitness evaluation. This indicator simultaneously considers convergence, diversity, and distribution to improve the efficiency of identifying promising candidate solutions, which significantly improves algorithm performance. The composite indicator-based candidate selection strategy is easy to achieve and computes efficiency. Component analysis experiments confirm the effect

Author

Zhen, Huixiang; Li, Xiaotong; Gong, Wenyin; Wang, Ling; Hu, Xiangyun

Published

2025

Language

EN