A Comparative Study of Artificial Bee Colony Algorithm

In: Computers and Technology

Submitted By rifkyali
Words 19739
Pages 79
Applied Mathematics and Computation 214 (2009) 108–132

Contents lists available at ScienceDirect

Applied Mathematics and Computation journal homepage: www.elsevier.com/locate/amc

A comparative study of Artificial Bee Colony algorithm
Dervis Karaboga *, Bahriye Akay
Erciyes University, The Department of Computer Engineering, Melikgazi, 38039 Kayseri, Turkey

a r t i c l e

i n f o

a b s t r a c t
Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by genetic algorithm, particle swarm optimization algorithm, differential evolution algorithm and evolution strategies. Results show that the performance of the ABC is better than or similar to those of other population-based algorithms with the advantage of employing fewer control parameters. Ó 2009 Elsevier Inc. All rights reserved.

Keywords: Swarm intelligence Evolution strategies Genetic algorithms Differential evolution Particle swarm optimization Artificial Bee Colony algorithm Unconstrained optimization

1. Introduction Population-based optimization algorithms find near-optimal solutions to the difficult optimization problems by motivation from nature. A common feature of all population-based algorithms is that the population consisting of possible solutions to the problem is modified by applying some operators on the solutions depending on the information of their fitness. Hence, the population is moved towards better solution areas of the search space. Two important classes of population-based optimization algorithms are evolutionary algorithms [1] and swarm intelligence-based algorithms [2]. Although Genetic Algorithm…...

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