Particle Swarm Optimization Research Base on Quantum Q-Learning Behavior

摘要

Quantum-behaved Particle Swarm Optimization algorithm is analyzed, contraction-expansion coefficient and its control method are studied. To the different performance characteristics with different coefficients control strategies, a control method of coefficient with Q-learning is proposed. The proposed method can tune the coefficient adaptively, and the whole optimization performance is increased. The comparison and analysis of results with the proposed method, constant coefficient control method, linear decreased coefficient control method and non-linear decreased coefficient control method is given based on CEC 2005 benchmark function.