An Improved Quantum-behaved Particle Swarm Optimization Algorithm Based on Chaos Theory Exerting to Particle Position

In this paper, we propose an improved quantum-behaved particle swarm optimization (QPSO), introducing chaos theory into QPSO and exerting logistic map to every particle position X(t) at a certain probability. In this improved QPSO, the logistic map is used to generate a set of chaotic offsets and produce multiple positions around X(t). According to their fitness, the particle's position is updated. In order to further enhance the diversity of particles, mutation operation is introduced into and acts on one dimension of the particle's position. What's more, the chaos and mutation probabilities are carefully selected. Through several typical function experiments, its result shows that the convergence accuracy of the improved QPSO is better than those of QPSO, so it is feasible and effective to introduce chaos theory and mutation operation into QPSO.