Overview of Algorithms for Swarm Intelligence

Shu-Chuan Chu; Huang Hsiang-Cheh; John Roddick; Jeng-Shyang Pan

doi:10.1007/978-3-642-23935-9_3

Overview of Algorithms for Swarm Intelligence

Shu-Chuan Chu, Huang Hsiang-Cheh, John Roddick, Jeng-Shyang Pan

Research output: Contribution to conference › Paper › peer-review

28 Citations (Scopus)

Abstract

Swarm intelligence (SI) is based on collective behavior of self-organized 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.

Original language	English
Pages	28-41
Number of pages	14
DOIs	https://doi.org/10.1007/978-3-642-23935-9_3
Publication status	Published - 27 Sep 2011
Event	3rd International Conference, ICCCI 2011 - Duration: 21 Sep 2011 → …

Conference

Conference	3rd International Conference, ICCCI 2011
Period	21/09/11 → …

Keywords

Ant Colony System (ACS)
Artificial Bee Colony (ABC)
Particle Swarm Optimization (PSO)
Swarm intelligence (SI)

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10.1007/978-3-642-23935-9_3

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