On the performance of multiple-objective genetic local search on the 0/1 knapsack problem - a comparative experiment

TLDR

Multiple-objective metaheuristics, especially evolutionary algorithms, have been extensively studied since 1985, yet comparative analyses on large-scale problems remain scarce. This study extends two prior comparative experiments on the multi‑objective 0/1 knapsack problem. We benchmark two MOGLS variants against the best previous algorithms on identical test instances. MOGLS achieves superior approximations of the nondominated set within the same number of function evaluations.

Abstract

Multiple-objective metaheuristics, e.g., multiple-objective evolutionary algorithms, constitute one of the most active fields of multiple-objective optimization. Since 1985, a significant number of different methods have been proposed. However, only few comparative studies of the methods were performed on large-scale problems. We continue two comparative experiments on the multiple-objective 0/1 knapsack problem reported in the literature. We compare the performance of two multiple-objective genetic local search (MOGLS) algorithms to the best performers in the previous experiments using the same test instances. The results of our experiment indicate that our MOGLS algorithm generates better approximations to the nondominated set in the same number of functions evaluations than the other algorithms.