一种多粒子群协同进化算法

为进一步提高多粒子群协同进化算法的寻优精度, 并有效改善粒子群易陷入局部极值及收敛速度慢的问题, 结合遗传算法较强的全局搜索能力和极值优化算法的局部搜索能力, 提出了一种改进的多粒子群协同进化算法. 对粒子群优化算法提出改进策略, 并在种群进化过程中, 利用遗传算法增加粒子的多样性及优良性, 经过一定次数的迭代, 利用极值优化算法加快收敛速度. 实验结果表明该算法具有较好的性能, 能够摆脱陷入局部极值点的问题, 并具有较快的收敛速度.

Cooperative Particles Swarm Optimization Algorithm

To improve the optimizing accuracy, and solve the problem of falling into local optima and the lower rate of convergence in cooperative particles swarm optimization, an improved cooperative particles swarm optimization algorithm is proposed. The proposed approach combines the strong global search ability of genetic algorithm and the excellent local search ability of extreme optimization algorithm. Firstly, an improved strategy is presented for particle swarm optimization. Then, the genetic algorithm is used to increase the diversity and optimal benign of the particles. After a certain iterations intervals, extreme optimization is adopted to accelerate the convergence. The experimental results show that the proposed approach can improve the optimal performance, escape from local optima, and enhance the rate of convergence.