A Glowworm Swarm Optimization Algorithm Based on Metropolis Criterion
Guangwei Zhao -, Yongquan Zhou, Qifang LuoYingju Wang - · International Journal of Advancements in Computing Technology · 2012
In order to overcome the glowworm swarm optimization algorithm is easy falling into the local optimal and has a low convergence precision. We shall the metropolis criterion embedded in the glowworm swarm optimization algorithm, a glowworm swarm optimization algorithm based on the metropolis criterion is proposed. We use the metropolis criterion to update the individual best position of glowworm, make the algorithm avoid falling into local optimum, thus increasing the algorithm’s ability to search the global optimum. Finally, through test six standard functions. The experiment results show that glowworm swarm optimization algorithm based on the metropolis criterion has better performance than glowworm swarm optimization (GSO) algorithm and artificial fish school algorithm (AFSA).