Title: An improved self-adaptive artificial bee colony algorithm for global optimisation
Authors: Anguluri Rajasekhar; Millie Pant
Department of Electrical Engineering, National Institute of Technology, Warangal, 506004, Andhra Pradesh, India
Department of Applied Science and Engineering, Indian Institute of Technology, Roorkee, 247667, Roorkee, India
Abstract: In this paper we propose an improved self-adaptive artificial bee colony algorithm (IS-ABC) for accurate numerical function optimisation. A modified self-adaptive mechanism based on 1/5th success rule is embedded in the structure of ABC to enhance the speed of the algorithm. The proposed algorithm has been tested on various numerical benchmark functions including the non-traditional functions proposed in CEC 2008 competition. To further validate performance of proposed algorithm we had also considered two challenging real world continuous optimisation problems. The results obtained with self-adaptive ABC have been compared with those obtained by new state-of-variants of PSO, DE, etc.
Keywords: artificial bee colony; ACB algorithm; global optimisation; Rechenberg's rule; self-adaptive algorithm.
Int. J. of Swarm Intelligence, 2014 Vol.1, No.2, pp.115 - 132
Date of acceptance: 26 Jul 2013
Available online: 01 Apr 2014