Next Article in Journal
Decline of Hesperia ottoe (Lepidoptera: Hesperiidae) in Northern Tallgrass Prairie Preserves
Next Article in Special Issue
Effect of Olfactory Stimulus on the Flight Course of a Honeybee, Apis mellifera, in a Wind Tunnel
Previous Article in Journal
Entomopathogenic Fungi Associated with Exotic Invasive Insect Pests in Northeastern Forests of the USA
Previous Article in Special Issue
Pollen Elicits Proboscis Extension but Does not Reinforce PER Learning in Honeybees
Insects 2013, 4(4), 646-662; doi:10.3390/insects4040646
Article

Honey Bees Inspired Optimization Method: The Bees Algorithm

1,* , 2
,
3
,
4
 and
3
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 / Revised: 2 October 2013 / Accepted: 28 October 2013 / Published: 6 November 2013
(This article belongs to the Special Issue Honey Bee Behavior)
View Full-Text   |   Download PDF [397 KB, 19 December 2013; original version 6 November 2013]   |   Browse Figures

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 honey bee; foraging behavior; waggle dance; bees algorithm; swarm intelligence; swarm-based optimization; adaptive neighborhood search; site abandonment; random search
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Share & Cite This Article

Further Mendeley | CiteULike
Export to BibTeX |
EndNote
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.

View more citation formats

Article Metrics

For more information on the journal, click here

Comments

Cited By

[Return to top]
Insects EISSN 2075-4450 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert