Abstract
Quantum-behaved particle swarm optimization (QPSO), motivated by analysis from particle swarm optimization (PSO) and quantum mechanics, has shown excellent performance in finding the optimal solutions for many optimization problems. In QPSO, the mean best position, defined as the average of the personal best positions of all the particles in a swarm, is employed as a global attractor to attract the particles to search solutions globally. This paper presents a comprehensive analysis of the mean best position and proposes several novel adaptive strategies to determine the position. In particular, four variants of mean best position are proposed to serve as global attractors and the corresponding parameter selection methods are also provided. Empirical studies on a suite of well-known benchmark functions are undertaken in order to make an overall performance comparison among the proposed methods and other QPSO and PSO variants. The simulation results show that the proposed QPSO algorithm have some advantages over the original QPSO and other PSO algorithms.
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