Honey Bees Inspired Optimization Method: The Bees Algorithm

Insects

Baris Yuce¹  Michael S. Packianather²  Ernesto Mastrocinque⁴  Duc Truong Pham³ 

[1] Institute of Sustainable Engineering, School of Engineering, Cardiff University, Queen’s Buildings, The Parade, Cardiff CF24 3AA, UK;Institute of Mechanical and Manufacturing Engineering, School of Engineering, Cardiff University, Queen’s Buildings, The Parade, Cardiff CF24 3AA, UK; E-Mail:;School of Mechanical Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; E-Mail:;Department of Industrial Engineering, University of Salerno, Via Ponte don Melillo 1, Fisciano 80046, Italy; E-Mails:

关键词: honey bee;    foraging behavior;    waggle dance;    bees algorithm;    swarm intelligence;    swarm-based optimization;    adaptive neighborhood search;    site abandonment;    random search;   

DOI  :  10.3390/insects4040646

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.