A cooperative quantum particle swarm optimization based on multiple groups

Abstract

Quantum-behaved particle swarm optimization (QPSO) is a novel variant of particle swarm optimization (PSO), inspired by quantum mechanics. Compared with traditional PSO, the QPSO algorithm guarantees global convergence and has less number of controlling parameters. However, QPSO is likely to get trapped into a local optimum because of using a single search strategy. This paper proposes a cooperative quantum particle swarm optimization (CGQPSO) algorithm based on multiple groups which apply different search strategies. The diversity of search strategies balances exploration and exploitation and avoids the local optimal problem. A cooperative mechanism, such as competition and cooperation, is introduced to implement the adaptive adjustment of a particle swarm. The dynamic adaptability of the particle swarm can adjust different search strategies according to a specific problem. The experimental results of 10 benchmark functions show that the proposed CGQPSO outperforms than other QPSO variants in terms of the performance and robustness.