Article: Firefly algorithm with random attraction Journal: International Journal of Bio-Inspired Computation (IJBIC) 2016 Vol.8 No.1 pp.33 - 41 Abstract: Firefly algorithm (FA) is a new meta-heuristic optimisation algorithm, which simulates the social behaviour of fireflies based on the flashing and attraction characteristics of fireflies. The standard FA employs a fully attracted model, in which each firefly is attracted by any other brighter firefly in the swarm. However, the fully attracted model may result in slow convergence rate because of too many attractions. In this paper, we propose a new firefly algorithm called FA with random attraction (RaFA), which employs a randomly attracted model. In RaFA, each firefly is attracted by another randomly selected firefly. In order to enhance the global search ability of FA, a concept of Cauchy jump is utilised. Experiments are conducted on a set of well-known benchmark functions. Simulation results show that RaFA outperforms the standard FA and two other improved FAs in terms of solution accuracy and robustness. Compared to the standard FA, RaFA has lower computational time complexity. Inderscience Publishers - linking academia, business and industry through research

Title: Firefly algorithm with random attraction

Authors: Hui Wang; Wenjun Wang; Hui Sun; Shahryar Rahnamayan

Addresses: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China ' School of Business Administration, Nanchang Institute of Technology, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China ' Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada

Abstract: Firefly algorithm (FA) is a new meta-heuristic optimisation algorithm, which simulates the social behaviour of fireflies based on the flashing and attraction characteristics of fireflies. The standard FA employs a fully attracted model, in which each firefly is attracted by any other brighter firefly in the swarm. However, the fully attracted model may result in slow convergence rate because of too many attractions. In this paper, we propose a new firefly algorithm called FA with random attraction (RaFA), which employs a randomly attracted model. In RaFA, each firefly is attracted by another randomly selected firefly. In order to enhance the global search ability of FA, a concept of Cauchy jump is utilised. Experiments are conducted on a set of well-known benchmark functions. Simulation results show that RaFA outperforms the standard FA and two other improved FAs in terms of solution accuracy and robustness. Compared to the standard FA, RaFA has lower computational time complexity.

Keywords: swarm intelligence; firefly algorithm; fully attracted model; randomly attracted model; random attraction; numerical optimisation; metaheuristics; convergence rate; Cauchy jump; simulation.

DOI: 10.1504/IJBIC.2016.074630

International Journal of Bio-Inspired Computation, 2016 Vol.8 No.1, pp.33 - 41

Received: 12 Jul 2013
Accepted: 22 Sep 2013
Published online: 10 Feb 2016 *

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Title: Firefly algorithm with random attraction

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