Int. J. of Artificial Intelligence and Soft Computing   »   2015 Vol.5, No.2

 

 

Title: Hybridisation of classical unidimensional search with ABC to improve exploitation capability

 

Authors: Pranav Dass; Shimpi Singh Jadon; Harish Sharma; Jagdish Chand Bansal; Kendall E. Nygard

 

Addresses:
Department of Computer Science and Operations Research, North Dakota State University, Fargo, USA
Moren Link Road, ABV-Indian Institute of Information Technology and Management, Rajasthan Technical University, Gwalior, 474015, India
Department of Computer Science, Rajasthan Technical University, Kota, 324010, India
Department of Mathematics, South Asian University, New Delhi, 110021, India
Department of Computer Science and Operations Research, North Dakota State University, Fargo, USA

 

Abstract: Artificial bee colony (ABC) optimisation algorithm is relatively a recent, fast and easy to implement population-based meta heuristic for optimisation. ABC has been proved a competitive algorithm with some popular swarm intelligence-based algorithms such as particle swarm optimisation, firefly algorithm and ant colony optimisation. However, it is observed that ABC algorithm is better at exploration but poor at exploitation. Due to large step size, the solution search equation of ABC has enough chance to skip the optimum. In order to balance this, ABC is hybridised with a local search called as classical unidimensional search (CUS). The proposed algorithm is named as hybridised ABC (HABC). In HABC, best solution of each iteration is further exploited in both its positive and negative direction in a predefined range which enhances the exploitation in ABC. The experiments are carried out on 15 test problems of different complexities and dimensions in order to prove the efficiency of proposed algorithm and compared with ABC. The results shows that hybridisation of CUS with ABC improves the performance of ABC.

 

Keywords: artificial bee colony; ABC; exploration-exploitation; local search; swarm intelligence; exploitation capability.

 

DOI: 10.1504/IJAISC.2015.070636

 

Int. J. of Artificial Intelligence and Soft Computing, 2015 Vol.5, No.2, pp.151 - 164

 

Date of acceptance: 03 Jan 2015
Available online: 15 Jul 2015

 

 

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