Stochastic Particle Swarm Optimization Algorithm with Embedded Cascading Chaotic Strategy
LI Sheng1, HE Ming-Hui2, LI Jian-Lin2, ZHANG Li2
1.Institute of Agricultural Mechanization, Xinjiang Academy of Agricultural Sciences, Urumqi 830091 2.Beijing Institute of Space Long March Vehicle, Beijing 100076
Abstract Aiming at the premature convergence problem frequently occurring in complex optimization problems of particle swarm optimization (PSO) algorithms, an iteration equation of stochastic particle swarm evolution is proposed by importing the disturbed track factor. The statistical behavior of particles produced by the equation is ensured to approach certain convergence centre, while their dependence on the address of previous generation appears to be stochastic.Thus, it is possible for particle swarm to jump quickly or migrate instead of being trapped in the local extremum at early evolution. Meanwhile, the cascading chaotic strategy and the symmetric extremum perturbation strategy are employed to further enhance local convergence velocity and globe search capacities, respectively. Experimental results indicate that stochastic chaotic PSO algorithm composed by the proposed equation and the improved strategies is better than other homologous particle swarm optimization algorithms in robustness, convergence speed and accuracy.
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2015, Vol. 28 
