title: Honey Bees Inspired Optimization Method: The Bees Algorithm.
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.
authors.affiliation: Institute of Sustainable Engineering, School of Engineering, Cardiff University, Queen's Buildings, The Parade, Cardiff CF24 3AA, UK. YuceB@cardiff.ac.uk.
authors.forename: Baris
authors.initials: B
authors.lastname: Yuce
authors.affiliation: Institute of Mechanical and Manufacturing Engineering, School of Engineering, Cardiff University, Queen's Buildings, The Parade, Cardiff CF24 3AA, UK. PackianatherMS@cardiff.ac.uk.
authors.forename: Michael S
authors.initials: MS
authors.lastname: Packianather
authors.affiliation: Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, Fisciano 84084, Italy. emastrocinque@unisa.it.
authors.forename: Ernesto
authors.initials: E
authors.lastname: Mastrocinque
authors.affiliation: School of Mechanical Engineering, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK. d.t.pham@bham.ac.uk.
authors.forename: Duc Truong
authors.initials: DT
authors.lastname: Pham
authors.affiliation: Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, Fisciano 84084, Italy. lambiase@unisa.it.
authors.forename: Alfredo
authors.initials: A
authors.lastname: Lambiase
keywords: adaptive neighborhood search; bees algorithm; foraging behavior; honey bee; random search; site abandonment; swarm intelligence; swarm-based optimization; waggle dance