Title: Dynamic network structured immune particle swarm optimisation with small-world topology

Authors: Yifei Sun; Licheng Jiao; Xiaozheng Deng; Rongfang Wang

Addresses: School of Physics and Information technology, Shaanxi Normal University, Xi'an, Shaanxi 710119, China ' Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi Province, 710071, China ' Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi Province, 710071, China ' Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Xidian University, Xi'an, Shaanxi Province, 710071, China

Abstract: Particle swarm optimisation (PSO) has attracted much attention and is used to wide applications in different fields in recent years because of its simple concept, easy implementation and quick convergence. However, it suffers from premature convergence since the population's diversity loses quickly. In this paper, a novel and efficient variant of PSO named DNIPSO is proposed which help the diversity of the swarm be preserved via the Newman-Watts small world network topology and the immune learning operator. Initially the topology of the population is the regular network. Then the Newman-Watts small world topology is formed gradually and the swarm evolves simultaneously. The optimisation process contains the population structure dynamics and particle immune learning two parts which mutually promoted effectively in whole population. Furthermore, the immune operator which is based on the clonal selection theory achieves a trade-off between exploration and exploitation abilities. Numerical experiments both on continuous unconstrained and constrained benchmark functions are used to test the performance of DNIPSO. Simulation results show it is effective and robust.

Keywords: particle swarm optimisation; PSO; Newman-Watts small-world model; artificial immune system; AIM; dynamic network structure; clonal selection; small-world topology; exploration; exploitation; simulation; metaheuristics; swarm intelligence.

DOI: 10.1504/IJBIC.2017.083100

International Journal of Bio-Inspired Computation, 2017 Vol.9 No.2, pp.93 - 105

Received: 24 Jul 2014
Accepted: 04 Oct 2014
Published online: 21 Mar 2017 *

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