Quantum Behaved Particle Swarm Optimization Algorithm Based on Artificial Fish Swarm

摘要

Quantum behaved particle swarm algorithm is a new intelligent optimization algorithm; the algorithm has less parameters and is easily implemented. In view of the existing quantum behaved particle swarm optimization algorithm for the premature convergence problem, put forward a quantum particle swarm optimization algorithm based on artificial fish swarm. The new algorithm based on quantum behaved particle swarm algorithm, introducing the swarmand following activities, meanwhile using the adaptive parameters, to avoid it falling into localextremum of population. The experimental results show the improved algorithm to improve the optimization ability of the algorithm.