Impact of diversity on bounded archives for multi-objective local search

2026-02-04

AI Summary

This work addresses the challenges of rapidly growing nondominated solution sets and the tendency of search processes to become trapped in local regions of the Pareto front in multi-objective optimization. To overcome the limitations of existing approaches that focus solely on diversity in objective space, the authors propose a novel method emphasizing diversity in decision space. The approach incorporates a bounded archive mechanism based on Hamming distance and is rigorously evaluated against adaptive grid and hypervolume-based archiving strategies. Experimental results demonstrate that the proposed algorithm not only maintains a controllable archive size but also significantly enhances both the distribution and convergence efficiency of multi-objective local search, outperforming current state-of-the-art methods.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

This work tackles two critical challenges related to the development of metaheuristics for Multi-Objective Optimization Problems (MOOPs): the exponential growth of non-dominated solutions and the tendency of metaheuristics to disproportionately concentrate their search on a subset of the Pareto Front. To counteract the first, bounded archives are employed as a strategic mechanism for effectively managing the increasing number of non-dominated solutions. Addressing the second challenge involves an in-depth exploration of solution diversity algorithms found in existing literature. Upon recognizing that current approaches predominantly center on diversity within the objective space, this research introduces innovative methods specifically designed to enhance diversity in the solution space. Results demonstrate the efficacy of the Hamming Distance Archiving Algorithm, one of the newly proposed algorithms for multi-objective local search, surpassing the performance of the Adaptive Grid Archiving and the Hypervolume Archiving, both drawn from the literature. This outcome suggests a promising avenue for enhancing the overall efficiency of metaheuristics employed for solving MOOPs.

Problem

• Multi-Objective Optimization
• Pareto Front
• Solution Diversity
• Bounded Archives
• Metaheuristics

Innovation

• solution space diversity
• Hamming Distance Archiving
• bounded archives
• multi-objective local search
• Pareto Front coverage