基于高斯-柯西变异的帝国竞争算法优化

Optimization of Empire Competition Algorithm Based on Gauss-Cauchy Mutation

为解决帝国竞争算法(Imperial Competitive Algorithm,ICA)竞争过程中收敛速度慢和易陷入局部最优的问题,提出了一种基于高斯-柯西变异的帝国竞争算法(Imperial Competitive Algorithm Based on Gauss-Cauchy Mutation,GCICA)。在ICA帝国竞争时引入高斯变异,加快竞争过程中的收敛速度;帝国灭亡后多样性减少且仅在小范围区域内进行寻优,引入柯西变异,使其跳出局部最优。分析引入高斯、柯西、高斯-柯西变异后的算法在多个典型基准测试函数上的仿真结果,GCICA的收敛速度和寻优精度都得到了提升。

In order to solve the problems of slow convergence speed and easy to fall into local optimum in the competition process of imperial competitive algorithm(ICA),a new imperial competitive algorithm based on Gauss-Cauchy mutation(GCICA)is proposed.Gauss mutation is introduced in ICA Empire competition to speed up the convergence speed in the competition process;after the Empire perishes,the diversity is reduced and only in a small area for optimization,and Cauchy mutation is introduced to make it jump out of local optimum.By analyzing the simulation results of the algorithm with Gauss,Cauchy and Gauss-Cauchy mutation on several typical benchmark functions,the convergence speed and optimization accuracy of GCICA are improved.