An Adaptive Chaos Embedded Particle Swarm Optimization Algorithm
Chaos particle swarm optimization (CPSO) can not guarantee the population multiplicity and the optimized ergodicity, because its algorithm parameters are still random numbers in form. This paper proposes a new adaptive chaos embedded particle swarm optimization (ACEPSO) algorithm that uses chaotic maps to substitute random numbers of the classical PSO algorithm so as to make use of the properties of stochastic and ergodicity in chaotic search and introduces an adaptive inertia weight factor for each particle to adjust its inertia weight factor adaptively in response to its fitness, which can overcome the drawbacks of CPSO algorithm that is easily trapped in local optima. The experiments with complex and Multi-dimensional functions demonstrate that ACEPSO outperforms the original CPSO in the global searching ability and convergence rate.
embedded optimization algorithm particle swarm chaos global optimization
Hua Rong
school of railway transportation shanghai institute of technology Shanghai 200235, China
国际会议
长春
英文
314-317
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）