一种具有区域影响的人工萤火虫算法

An Artificial Glowworm Swarm Optimization Algorithm with Area of Influence

受到自然界中萤火虫通过荧光进行信息交流的群体行为的启示,萤火虫算法被提出。它是一种新颖的仿生群智能优化算法。基本的萤火虫算法中,萤火虫个体间存在协作不足,易陷入局部最优的缺陷;考虑到萤火虫个体的区域影响作用,提出一种更接近社会上信息传递系统的萤火虫算法。该算法综合考虑了萤火虫个体的历史最优位置和萤火虫群体的历史最优位置对当前位置的影响作用,使相距较近的萤火虫个体能很快地得到信息并受其影响。实验仿真结果表明,区域影响下的萤火虫算法性能有了显著提高。

Inspired by social behavior of glowworm swarm and the phenomenon of bioluminescent communication, Glowworm Swarm Optimization （ GSO） algorithm is developed as a novel bionic swarm intelligence optimization method. Based on the analysis of short-coming of basic GSO such as lack of collaboration among glowworm and easily falling into local optimal,and considering the influence of area of glowworm individual,propose a new GSO which is more close to social glowworm swarm system. The algorithm takes local optimal solutions and global optimal solution into account generally,which gets information quickly and can be affected among glow-worms which are nearby. The simulation results show that the GSO performance with area of influence has greatly improved.