Title: Relationships of swarm intelligence and artificial immune system

Authors: Renbin Xiao; Tinggui Chen

Addresses: Institute of Systems Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China ' College of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, Zhejiang 310018, China

Abstract: Swarm intelligence (SI) and artificial immune system (AIS) are both derived from imitation of nature biology system. Their common characteristic is that they both have simple individuals but appear emergence characteristic in population level through interaction among individuals. In order to explore inherent similarity and difference of complex system, we take two typical forms of swarm intelligence (ant colony system and particle swarm optimisation), and AIS as objectives to study this characteristic in this paper. First, we discuss the similarity between two biology systems from system structure and operation mechanism. In addition, we also illustrate the difference between two systems from algorithm design, individual diversity and shape space. At the end of the paper, numerical experiment is used to test the performance of swarm intelligence techniques and AIS, where benchmark test functions include unimodal and multimodal function optimisation problems. Besides, combined with a concrete example, travelling salesman problem (TSP), the generality and feature of these two systems in solving complex problems are discussed in detail. The objective of the paper is to set up inherent connection and difference between two unlike systems, which not only has important theoretical significance but also has significant actual value to reveal production and operation mechanism of human intelligence.

Keywords: bio-inspired computation; swarm intelligence; artificial immune system; AIS; complex systems; relationship; similarity; ant colony optimisation; ACO; particle swarm optimisation; PSO; travelling salesman problem; TSP.

DOI: 10.1504/IJBIC.2013.053057

International Journal of Bio-Inspired Computation, 2013 Vol.5 No.1, pp.35 - 51

Received: 26 Jan 2013
Accepted: 28 Jan 2013
Published online: 31 Mar 2014 *

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