A hybrid glowworm swarm optimization algorithm to solve constrained multimodal functions optimization

Abstract

In this paper, a novel hybrid glowworm swarm optimization (HGSO) algorithm is proposed. The HGSO algorithm embeds predatory behaviour of artificial fish swarm algorithm (AFSA) into glowworm swarm optimization (GSO) algorithm and combines the GSO with differential evolution on the basis of a two-population co-evolution mechanism. In addition, to overcome the premature convergence, the local search strategy based on simulated annealing is applied to make the search of GSO approach the true optimum solution gradually. Finally, several benchmark functions show that HGSO has faster convergence efficiency and higher computational precision, and is more effective for solving constrained multi-modal function optimization problems.

Keywords:

• glowworm swarm optimization
• artificial fish swarm
• differential evolution
• hybrid optimization algorithm
• multimodal function optimization

AMS Subject Classification:

• 68T20
• 68W20

Acknowledgements

The authors are very grateful to the referees for their valuable comments and suggestions. This work is supported by National Science Foundation of China (61165015), key project of Guangxi High School Science Foundation (20121ZD008) and the funded by open research fund program of key lab of intelligent perception and image understanding of ministry of Education of China (IPIU01201100).