Improving Quantum-Behaved Particle Swarm Optimization by Simulated Annealing

Quantum-behaved Particle Swarm Optimization (QPSO) is a global convergence guaranteed search method, which introduced quantum theory into original Particle Swarm Optimization (PSO). While Simulated Annealing (SA) is another important stochastic optimization with the ability of probabilistic hill-climbing. In this paper, the mechanism of Simulated Annealing is introduced into the weak selection implicit in our QPSO algorithm, which effectively employs both the ability to jump out of the local minima in Simulated Annealing and the capacity of searching the global optimum in QPSO algorithm. The experimental results show that the proposed hybrid algorithm increases the diversity of the population in the search process and improves its precision in the latter period of the search.