一种基于混沌云模型的人工萤火虫优化算法

Glowworm Swarm Optimization Algorithm Based on Chaos Cloud Model

针对基本萤火虫优化(GSO)算法存在的求解精度不高、收敛速度慢、易陷入局部极小等缺陷,引入混沌算法和云模型算法对GSO的进化机制进行优化,提出一种基于混沌云模型的萤火虫优化(CCMGSO)算法.该算法在进化过程中应用云模型算法对优秀萤火虫进行局部发掘求精,增加求解精度;应用混沌算法对普通萤火虫进行全局探索寻优,避免陷入局部最优.通过基准函数的仿真实验表明,与基本萤火虫算法、最大最小荧光素值萤火虫算法相比,CCMGSO算法表现更优,且具有求解精度高、收敛速度快和寻找全局最优能力强等优点.

To solve the problems of glowworm swarm optimization（ GSO） algorithm in lowcomputational accuracys,lowconvergence speed,and easy to fall into local optimizaiton,the chaos algorithm and cloud model algorithm were introduced to optimize the evolution mechanism of GSO and chaos cloud model glowworm swarm optimization（ CCMGSO） algorithm was proposed. In the evolutionary process,the cloud model algorithm and excellent glowworms were applied to local excavation refinement,to increase the accuracy of solution,meanwhile the chaos algorithm and ordinary glowworms were used to exploration of global optimization,to avoid falling into local optimal. Through the functions testing,experiment results showthat the proposed algorithm is superior to GSO and GSO based on max-min luciferin in computational accuracys,covergence speed and find the global optimum.