Authors:
Bui Ngoc Tam
;
Pham Ngoc Hieu
and
Hiroshi Hasegawa
Affiliation:
Shibaura Institute of Technology, Japan
Keyword(s):
Artificial Bee Colony, Differential Evolution, Global Search, Hybrid Optimization Methods, Local Search, Multi-peak Problems.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Co-Evolution and Collective Behavior
;
Computational Intelligence
;
Evolutionary Computing
;
Evolutionary Multiobjective Optimization
;
Hybrid Systems
;
Memetic Algorithms
;
Soft Computing
;
Swarm/Collective Intelligence
Abstract:
In this paper, we investigate the hybridization of a swarm intelligence algorithm and an evolutionary algorithm, namely, the Artificial Bee Colony (ABC) algorithm and Differential Evolution (DE), to solve continuous optimization problems. This Hybrid Integration of DE and ABC (HIDEABC) technique is based on integrating the DE algorithm with the principle of ABC to improve the neighborhood search for each particle in ABC. The swarm intelligence of the ABC algorithm and the global information obtained by the DE population approach facilitate balanced exploration and exploitation using the HIDEABC algorithm. All algorithms were applied to five benchmark functions and were compared using several different metrics.