An Improved Glowworm Swarm Optimization Algorithm Based on Parallel Hybrid Mutation

摘要

Glowworm swarm optimization (GSO) algorithm is a novel algorithm based on swarm intelligence and inspired from light emission behavior of glowworms to attract a peer or prey in nature. The main application of this algorithm is to capture all local optima of multimodal function. GSO algorithm has shown some such weaknesses in global search as low accuracy computation and easy to fall into local optimum. In order to overcome above disadvantages of GSO, this paper presented an improved GSO algorithm, which called parallel hybrid mutation glowworm swarm optimization (PHMGSO) algorithm. Experimental results show that PHMGSO has higher calculation accuracy and convergence faster speed compared to standard GSO and PSO algorithms.