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

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

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) and Xu-Yan Tu (School of Computer and Communications Engineering, University of Science and Technology Beijing, Beijing, China)
DOI: 10.4018/IJCINI.2018010104

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.
Article Preview

2. Swarm Intelligence Algorithm

2.1. Basic Glowworm Swarm Optimization

In nature, seemingly simple life-year individual, group activities have certain characteristics. Birds in the absence of migration, there is no centralized control, but to complete the collective action, ant through the sowing pheromone to remember the path of the way to inform the community to find food, bees through the different division of labor to exchange nectar source position information. The Glowworm Swarm Optimization simulates the luminous characteristics of the glowworms in nature to be randomly optimized, and the individuals with strong fluorescence intensity attract weak individuals with weak fluorescence. The Glowworm Swarm Optimization (GSO) is a new group of intelligent optimization algorithms based on multi-peak function optimization proposed by K.N. Krishnanad and D. Hheose in 2005, the algorithm has been successful in the optimization of complex functions. The Glowworm Swarm Optimization mimics the behavior of glowworms predators and spouses in nature, and glowworm individuals use their luminescent properties to find better individuals in their search areas to optimize their position. Nowadays, the Glowworm Swarm Optimization is attracting more and more attention and has been applied to many fields, for example, in the application of swarm robots(Krishnanand, & Ghose, 2005), leakage of harmful gas or nuclear leakage detection(Krishnanand, & Ghose, 2006), detection of multiple beam source localization(Krishnanand, 2007), and multi-modal optimization problems (Krishnanand, & Ghose, 2009, Krishnanand, & Ghose, 2009, Krishnanand, & Ghose, 2006), etc., the better application of this algorithm has attracted the attention of scholars at home and abroad, and has become a new research hot topic in the field of intelligent computing.

Complete Article List

Search this Journal:
Reset
Open Access Articles: Forthcoming
Volume 13: 4 Issues (2019): Forthcoming, Available for Pre-Order
Volume 12: 4 Issues (2018): 3 Released, 1 Forthcoming
Volume 11: 4 Issues (2017)
Volume 10: 4 Issues (2016)
Volume 9: 4 Issues (2015)
Volume 8: 4 Issues (2014)
Volume 7: 4 Issues (2013)
Volume 6: 4 Issues (2012)
Volume 5: 4 Issues (2011)
Volume 4: 4 Issues (2010)
Volume 3: 4 Issues (2009)
Volume 2: 4 Issues (2008)
Volume 1: 4 Issues (2007)
View Complete Journal Contents Listing