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

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