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

TL;DR

This paper investigates how diversity impacts bounded archives in multi-objective local search, proposing new algorithms that improve solution management and diversity in the solution space, leading to better optimization performance.

Contribution

It introduces innovative diversity algorithms focused on the solution space and demonstrates the effectiveness of the Hamming Distance Archiving Algorithm over existing methods.

Findings

Hamming Distance Archiving outperforms existing algorithms

Enhanced diversity improves metaheuristic efficiency

New algorithms effectively manage non-dominated solutions

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.