Heuristic Initialization and Knowledge-based Mutation for Large-Scale Multi-Objective 0-1 Knapsack Problems

Cheng Gong, Yang Nan, Lie Meng Pang, Hisao Ishibuchi, Qingfu Zhang

Abstract: Recently, there has been a growing interest in large-scale multiobjective optimization problems within the evolutionary multiobjective optimization (EMO) community. These problems involve hundreds or thousands of decision variables and multiple conflicting objectives, which pose significant challenges for conventional EMO algorithms (EMOAs). It is generally believed that EMOAs have difficulty in efficiently finding good non-dominated solutions as the number of decision variables increases. To address this issue, in this paper, we propose a novel method that incorporates heuristic initialization and knowledge-based mutation into EMOAs for solving large-scale multi-objective 0-1 knapsack problems. Various large-scale multi-objective 0-1 knapsack problems with an arbitrary number of constraints are generated as test problems to evaluate the effectiveness of the proposed method. Experimental results show that the proposed novel initialization and mutation method significantly improves the performance of the original EMOAs in terms of both the convergence speed in early generations and the quality of the final population.