Particle Swarm Optimization Based on Opposition-based Learning and Quantum Optimization

In order to overcome the drawback of standard particle swarm algorithm which is easy to fall into local optimum,an improved particle swarm optimization algorithm is proposed combined with quantum optimization and opposition-based learning.There are three aspects that improve the quantum particle swarm algorithm performance: the initialization of population,population jumps and the best individual in the population of the local improvement.The improved algorithm can effectively avoid particle swarm into local optimum and accelerated population to the optimal position of the convergence.The numerical experiments show that the hybrid algorithm has high performance in different function optimization