Title: New progresses in swarm intelligence-based computation

Authors: Haibin Duan; Qinan Luo

Addresses: State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, 100191, China ' State Key Laboratory of Virtual Reality Technology and Systems, School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing, 100191, China

Abstract: Nature is a great and immense source of inspiration for solving complex problems in the real world. The well-known examples in nature for swarms are bird flocks, fish schools and the colony of social insects. Birds, ants, bees, fireflies, bats, and pigeons are all bringing us various inspirations for swarm intelligence. In 1990s, swarm intelligence algorithms based on ant colony have highly attracted the interest of researchers. During the past two decades, several new algorithms have been developed depending on different intelligent behaviours of natural swarms. This review presents a comprehensive survey of swarm intelligence-based computation algorithms, which are ant colony optimisation, particle swarm optimisation, artificial bee colony, firefly algorithm, bat algorithm, and pigeon inspired optimisation. Future orientations are also discussed thoroughly.

Keywords: swarm intelligence; bio-inspired computation; ant colony optimisation; ACO; particle swarm optimisation; PSO; artificial bee colony; ABC; firefly algorithm; bat algorithm; pigeon inspired optimisation; PIO; intelligent behaviours; natural swarms.

DOI: 10.1504/IJBIC.2015.067981

International Journal of Bio-Inspired Computation, 2015 Vol.7 No.1, pp.26 - 35

Received: 28 Jun 2014
Accepted: 01 Jul 2014
Published online: 12 Mar 2015 *

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