Diversity-preserving quantum particle swarm optimization for the multidimensional knapsack problem

Quantum particle swarm optimization is a population-based metaheuristic that becomes popular in recent years in the field of binary optimization. In this paper, we investigate a novel quantum particle swarm optimization algorithm, which integrates a distanced-based diversity-preserving strategy for population management and a local optimization method based on variable neighborhood descent for solution improvement. We evaluate the proposed method on the classic NP-hard 0-1 multidimensional knapsack problem. We present extensive computational results on the 270 benchmark instances commonly used in the literature to show the competitiveness of the proposed algorithm compared to several state-of-the-art algorithms. The ideas of using the diversity-preserving strategy and the probabilistic application of a local optimization procedure are of general interest and can be used to reinforce other quantum particle swarm algorithms. (C) 2020 Elsevier Ltd. All rights reserved.