Research and Application of Adaptive Step Mechanism for Glowworm Swarm Optimization Algorithm

Hong-Bo Wang, Ke-Na Tian, Xue-Na Ren and Xu-Yan Tu
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Hong-Bo Wang: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
Ke-Na Tian: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
Xue-Na Ren: Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China
Xu-Yan Tu: School of Computer and Communications Engineering, University of Science and Technology Beijing, Beijing, China

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2018, vol. 12, issue 1, 42-59

Abstract: Glowworm Swarm Optimization Algorithm (GSO) is one of new swarm intelligence optimization algorithms in recent years. Its main idea comes from the cooperative behavior source among individuals during the process of courtship and foraging. In this article, in order to improve convergence speed in the late iteration, avoid the algorithm falling into local optimum, and reduce isolated nodes, the Adaptive Step Mechanism Glowworm Swarm Optimization (ASMGSO) is proposed. The main idea of ASMGSO algorithm is as follows: (1) On the basis of SMGSO algorithm, isolated nodes carry out bunching operator firstly, that is to say they are moving to the central position of the group. If the new position is not better than the current position, then isolated nodes perform mutation operation. (2) At the same time, the fixed step mechanism has been improved. The effectiveness of the proposed ASMGSO algorithm is verified through several classic test functions and application in Distance Vector-Hop.

Date: 2018
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