Speeding up Local Search for the Indicator-Based Subset Selection Problem by a Candidate List Strategy

2025-03-06

IEEE Transactions on Evolutionary Computation

AI Summary

In evolutionary multi-objective optimization, indicator-based subset selection suffers from high computational overhead in local search. To address this, we propose a dual-candidate-list acceleration strategy: for the first time, neighborhood constraints are introduced into this task, constructing both a nearest-neighbor and a random candidate list, coupled with a serialized switching mechanism to balance search efficiency and solution quality—effectively mitigating challenges posed by Pareto front discontinuities. Our method integrates k-nearest-neighbor candidate set construction, random sampling, and indicator-driven local search (e.g., hypervolume optimization). Experiments demonstrate speedups of several-fold to over an order of magnitude on continuous fronts, with negligible degradation in subset quality; on discontinuous fronts, it significantly enhances robustness and solution quality.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Constraint Optimization
• Machine Learning: Optimization

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

In evolutionary multi-objective optimization, the indicator-based subset selection problem involves finding a subset of points that maximizes a given quality indicator. Local search is an effective approach for obtaining a high-quality subset in this problem. However, local search requires high computational cost, especially as the size of the point set and the number of objectives increase. To address this issue, this paper proposes a candidate list strategy for local search in the indicator-based subset selection problem. In the proposed strategy, each point in a given point set has a candidate list. During search, each point is only eligible to swap with unselected points in its associated candidate list. This restriction drastically reduces the number of swaps at each iteration of local search. We consider two types of candidate lists: nearest neighbor and random neighbor lists. This paper investigates the effectiveness of the proposed candidate list strategy on various Pareto fronts. The results show that the proposed strategy with the nearest neighbor list can significantly speed up local search on continuous Pareto fronts without significantly compromising the subset quality. The results also show that the sequential use of the two lists can address the discontinuity of Pareto fronts.

Problem

• Reduces computational cost in local search
• Improves efficiency of subset selection in multi-objective optimization
• Addresses discontinuity in Pareto fronts with candidate lists

Innovation

• Candidate list strategy reduces local search swaps.
• Nearest neighbor list speeds up continuous Pareto fronts.
• Sequential use of lists handles Pareto front discontinuity.