Composite Indicator-Guided Infilling Sampling for Expensive Multi-Objective Optimization

2025-03-28

AI Summary

For computationally expensive, budget-constrained multi-objective optimization problems, this paper proposes a composite-metric-guided surrogate-assisted infill sampling framework. Methodologically, we integrate a lightweight, novel composite performance metric—uniquely combining convergence, diversity, and distribution quality—into the NSGA-III framework, coupled with high-fidelity surrogate models for efficient prediction and candidate selection. This enables tri-objective synergistic guidance for precise solution screening. Experimental evaluation on standard benchmark suites demonstrates that our approach significantly outperforms five state-of-the-art algorithms in terms of both final solution quality and convergence speed under limited function evaluations. Ablation studies validate the complementary roles and necessity of each constituent metric component. Overall, this work establishes a new paradigm for expensive multi-objective optimization: interpretable, low-overhead, and high-efficiency—bridging theoretical rigor with practical deployability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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 effectiveness of each element in the composite performance indicator. Comparative experiments on benchmark problems demonstrate that the proposed algorithm outperforms five state-of-the-art expensive multi-objective optimization algorithms.

Problem

• Balancing convergence, diversity, and distribution in expensive multi-objective optimization
• Selecting promising candidate solutions under limited evaluation budgets
• Improving algorithm performance via composite indicator-guided infilling sampling

Innovation

• Uses NSGA-III for surrogate-based exploration
• Introduces composite indicator for candidate selection
• Balances convergence, diversity, and distribution efficiently