Overview of Algorithms for Swarm Intelligence

Overview of Algorithms for Swarm Intelligence
Shu-Chuan Chu1, Hsiang-Cheh Huang2, John F. Roddick1, and Jeng-Shyang Pan3
1

School of Computer Science, Engineering and Mathematics,
Flinders University of South Australia, Australia
2
National University of Kaohsiung, 700 University Road, Kaohsiung 811, Taiwan, R.O.C.
3
National Kaohsiung University of Applied Sciences, 415 Chien-Kung Road,
Kaohsiung 807, Taiwan, R.O.C.

Abstract. Swarm intelligence (SI) is based on collective behavior of selforganized systems. Typical swarm intelligence schemes include Particle Swarm
Optimization (PSO), Ant Colony System (ACS), Stochastic Diffusion Search
(SDS), Bacteria Foraging (BF), the Artificial Bee Colony (ABC), and so on.
Besides the applications to conventional optimization problems, SI can be used in controlling robots and unmanned vehicles, predicting social behaviors, enhancing the telecommunication and computer networks, etc. Indeed, the use of swarm optimization can be applied to a variety of fields in engineering and social sciences. In this paper, we review some popular algorithms in the field of swarm intelligence for problems of optimization. The overview and experiments of PSO, ACS, and ABC are given. Enhanced versions of these are also introduced. In addition, some comparisons are made between these algorithms.
Keywords. Swarm intelligence (SI), Particle Swarm Optimization (PSO), Ant
Colony System (ACS), Artificial Bee Colony (ABC).

1

Introduction

People learn a lot from Mother Nature. Applying the analogy to biological systems with lots of individuals, or swarms, we are able to handle the challenges in the algorithm and application with optimization techniques. In this paper, we focus on the overview of several popular swarm intelligence algorithms, pointing out their concepts, and proposing some enhancements of the algorithms with the results of our research group.
Swarm intelligence, according to [1], is the emergent collective

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