组织进化粒子群数值优化算法

Organizational Evolutionary Particle Swarm Optimization for Numerical Optimization

为充分利用粒子的通讯、响应、协作和自学习能力等特性,克服算法早熟收敛,本文提出一种组织进化粒子群算法.该算法将进化操作直接作用在组织上,通过组织间的相互竞争、协作,最终达到全局优化的目的,且证明算法的全局收敛性.实验中,用12个无约束标准测试函数对算法性能进行测试,与其它算法进行比较,并对算法中的参数进行分析.结果表明,本文算法无论在解的质量上还是在计算复杂度上都明显优于其它算法.参数分析表明该算法具有性能稳定、成功率高、对参数不敏感等优良特性.

An organizational evolutionary particle swarm optimization （OEPSO） is presented. The evolutional operations are acted on organizations directly in the algorithm. The global convergence is gained through competition and cooperation among the organizations, and the mathematic convergence is given. In the experiments, OEPSO is tested on 12 unconstrained benchmark problems, and compared with FEP and three algorithms based on the PSO . In addition , the effects of parameters in the algorithm are analyzed. The results indicate that OEPSO performs better than other algorithms both in solution quality and computational complexity. The analyses of parameters show OEPSO has stable performance and high success ratio, and it is insensitive to parameters.