一种自适应指导的文化粒子群算法

针对文化粒子群算法中影响函数对群体空间的全局变异操作，易导致粒子群算法结构失效及不易收敛的缺点，将群体适应度方差引入到群体空间，提出一种自适应指导的文化粒子群算法。算法通过计算群体适应度方差判断群体空间状态，当算法陷入局部最优时，自适应地利用影响函数对群体空间进行变异更新，从而有效发挥了文化粒子群算法“双演化双促进”机制。将该算法与基本粒子群算法（PSO）、文化粒子群算法（CPSO）和自适应变异粒子群算法（AMPSO）进行比较，实验结果证明该算法不仅具有较好的全局收敛性，算法收敛速度和稳定性也都有显著提高。

Cultural particle swarm optimization algorithm with adaptive guidance

In CPSO algorithm,the mution operator adopted by influence function may disturb the structure and convergence of PSO algorithm in population space.A new CPSO algorithm with adaptive guidance which colony fitness variance intro- duced into population space is proposed in this paper.The population space is prone to get into local best position in the last period of evolution.By calculating the colony fitness variance,decisions are maken whether to have mutate operation on population space.The improved algorithm can make better use of mechanism of dual evolution and dual promotion in CPSO algorithm.Comparison of the performance of the proposed approach with PSO algorithm,CPSO algorithm and AMPSO algo- rithm is experimented.The simulation results of typical complex function optimization problems show that the improved algo- rithm can not only effectively solve the premature convergence problem,but also significantly speed up the convergence and improve the stability.