An adaptive artificial bee colony algorithm for global optimization
Date
2015-11-15
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Science
Abstract
Artificial bee colony algorithm (ABC) is a recently introduced swarm based meta heuristic algorithm. ABC mimics the foraging behavior of honey bee swarms. Original ABC algorithm is known to have a poor exploitation performance. To remedy this problem, this paper proposes an adaptive artificial bee colony algorithm (AABC), which employs six different search rules that have been successfully used in the literature. Therefore, the AABC benefits from the use of different search and information sharing techniques within an overall search process. A probabilistic selection is applied to deterinine the search rule to be used in generating a candidate solution. The probability of selecting a given search rule is further updated according to its prior performance using the roulette wheel technique. Moreover, a ineinoly length is introduced corresponding to the maximum number of moves to reset selection probabilities. Experiments are conducted using well-known benchmark problems with varying dimensionality to compare AABC with other efficient ABC variants. Computational results reveal that the proposed AABC outperforms other novel ABC variants.
Description
Keywords
Adaptive search, Artificial bee colony algorithm, Global optimization, Efficient, Evolutionary algorithms, Global optimization, Heuristic algorithms, Optimization, Adaptive search, Artificial bee colony algorithms, Artificial bee colony algorithms (ABC), Bench-mark problems, Computational results, Information sharing, Meta heuristic algorithm, Selection probabilities, Algorithms
Citation
Yurtkuran, A. ve Emel, E. (2015). "An adaptive artificial bee colony algorithm for global optimization". Applied Mathematics and Computation, 271, 1004-1023.