In this paper, a novel hybrid glowworm swarm optimization (HGSO) algorithm is proposed. Firstly, the presented algorithm embeds predatory behavior of artificial fish swarm algorithm (AFSA) into glowworm swarm optimization (GSO) algorithm and combines the improved GSO with differential evolution (DE) on the basis of a two-population co-evolution mechanism. Secondly, under the guidance of the feasibility rules, the swarm converges towards the feasible region quickly. In addition, to overcome premature convergence, the local search strategy based on simulated annealing (SA) is used and makes the search near the true optimum solution gradually. Finally, the HGSO algorithm is for solving constrained engineering design problems. The results show that HGSO algorithm has faster convergence speed, higher computational precision, and is more effective for solving constrained engineering design problems.
Zhou, Yongquan; Zhou, Guo; and Zhang, Junli
"A Hybrid Glowworm Swarm Optimization Algorithm for Constrained Engineering Design Problems,"
Applied Mathematics & Information Sciences: Vol. 07:
1, Article 47.
Available at: https://digitalcommons.aaru.edu.jo/amis/vol07/iss1/47