Overview of Algorithms for Swarm Intelligence

Topics: Ant colony optimization, Particle swarm optimization, Artificial intelligence Pages: 26 (4410 words) Published: March 4, 2014
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
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).



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 intelligence of groups of simple agents. With swarm intelligence, the developed algorithms need to be flexible to internal and external changes, to be robust when some individuals fail, to be decentralized and self-organized [2]. In the rest of the paper, we will address several popular algorithms based on these concepts, including Particle Swarm Optimization (PSO), Ant Colony System (ACS), and Artificial Bee Colony (ABC) algorithms in Sec. 2, and we present the improvements of these algorithms based on our existing works in Sec. 3. Selected simulation results and comparisons are also provided in Sec. 4. Finally, we conclude this paper in Sec. 5. P. Jędrzejowicz et al. (Eds.): ICCCI 2011, Part I, LNCS 6922, pp. 28–41, 2011. © Springer-Verlag Berlin Heidelberg 2011

Overview of Algorithms for Swarm Intelligence



Swarm Intelligence Algorithms

In this section, we introduce the concept and implementation of several popular algorithm for swarm intelligence optimization, including particle swarm optimization (PSO), ant colony system (ACS), and Artificial Bees Colony (ABC) algorithms. 2.1

Particle Swarm Optimization (PSO)

Particle swarm optimization (PSO) was first introduced by Kennedy and Eberhart [3,4]. It is a relatively new stochastic optimization technique that can simulate the swarm behavior of birds flocking. In PSO, an individual in the swarm, called a particle, represents a potential solution. Each particle has a fitness value and a velocity, and it learns the experiences of the swarm to search for the global optima [5]. Traditional PSO can be depicted in Fig. 1. They include (1) particle initialization, (2) velocity updating, (3) particle position updating, (4) memory updating, and (5) termination checking. These steps are described as follows.

Fig. 1. Procedures for particle swarm optimization


Initialization. We first decide how many particles used to solve the problem. Every particle has its own position, velocity and best solution....

References: 2. Bonabeau, E.: Swarm Intelligence. In: O’Reilly Emerging Technology Conference (2003)
Overview of Algorithms for Swarm Intelligence
IEEE Press, New York (1995)
6. Colorni, A., Dorigo, M., Maniezzo, V.: Distributed Optimization by Ant Colonies. In: Varela, F., Bourgine, P. (eds.) First Eur. Conference Artificial Life, pp. 134–142 (1991)
53–66 (1997)
Information Sciences 167, 63–76 (2004)
11. Passino, K.M.: Biomimicry of Bacterial Foraging for Distributed Optimization and Control. IEEE Control Systems Magazine 22, 52–67 (2002)
PRICAI 2006. LNCS (LNAI), vol. 4099, pp. 854–858. Springer, Heidelberg (2006)
16. Tsai, P.W., Luo, R., Pan, S.T., Pan, J.S., Liao, B.Y.: Artificial Bee Colony with Forwardcommunication Strategy. ICIC Express Letters 4, 1–6 (2010)
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