Quantum Behaved Particle Swarm Optimization with Neighborhood Search for Numerical Optimization

Quantum-behaved particle swarm optimization (QPSO) algorithm is a new PSO variant, which outperforms the original PSO in search ability but has fewer control parameters. However, QPSO as well as PSO still suffers from premature convergence in solving complex optimization problems. The main reason is that new particles in QPSO are generated around the weighted attractors of previous best particles and the global best particle. This may result in attracting too fast. To tackle this problem, this paper proposes a new QPSO algorithm called NQPSO, in which one local and one global neighborhood search strategies are utilized to balance exploitation and exploration. Moreover, a concept of opposition-based learning (OBL) is employed for population initialization. Experimental studies are conducted on a set of well-known benchmark functions including multimodal and rotated problems. Computational results show that our approach outperforms some similar QPSO algorithms and five other state-of-the-art PSO variants