A Particle Swarm Optimization Algorithm Based on Hyper-Chaotic Sequences

摘要

Based on classical PSO (abbreviated for particle swarm optimization) algorithm and quantum theory, this paper proposes an improved quantum particle swarm optimization algorithm - zbQPSO (abbreviated for zhao Bezier quantum-behaved PSO) algorithm. Identical particle system is introduced to update the position of particle, hyper-chaotic thought introduced to chaotic search for every particle and average search length thought of search algorithm was introduced to improve the full and local searching ability, convergence rate and calculating precision for elementary particle swarm. The calculation results for classical function show that capability of improved algorithm is superior to classical PSO algorithm and quantum PSO algorithm.