Insects 2013, 4(4), 646-662; doi:10.3390/insects4040646
Article

Honey Bees Inspired Optimization Method: The Bees Algorithm

1 Institute of Sustainable Engineering, School of Engineering, Cardiff University, Queen's Buildings, The Parade, Cardiff CF24 3AA, UK 2 Institute of Mechanical and Manufacturing Engineering, School of Engineering, Cardiff University, Queen's Buildings, The Parade, Cardiff CF24 3AA, UK 3 Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, Fisciano 84084, Italy 4 School of Mechanical Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
* Author to whom correspondence should be addressed.
Received: 1 July 2013; in revised form: 2 October 2013 / Accepted: 28 October 2013 / Published: 6 November 2013
(This article belongs to the Special Issue Honey Bee Behavior)
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Abstract: Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.
Keywords: honey bee; foraging behavior; waggle dance; bees algorithm; swarm intelligence; swarm-based optimization; adaptive neighborhood search; site abandonment; random search

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MDPI and ACS Style

Yuce, B.; Packianather, M.S.; Mastrocinque, E.; Pham, D.T.; Lambiase, A. Honey Bees Inspired Optimization Method: The Bees Algorithm. Insects 2013, 4, 646-662.

AMA Style

Yuce B, Packianather MS, Mastrocinque E, Pham DT, Lambiase A. Honey Bees Inspired Optimization Method: The Bees Algorithm. Insects. 2013; 4(4):646-662.

Chicago/Turabian Style

Yuce, Baris; Packianather, Michael S.; Mastrocinque, Ernesto; Pham, Duc T.; Lambiase, Alfredo. 2013. "Honey Bees Inspired Optimization Method: The Bees Algorithm." Insects 4, no. 4: 646-662.

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