Development and investigation of efficient artificial bee colony algorithm.pdf

Development and investigation of efficient artificial bee colony algorithm.pdf

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Development and investigation of efficient artificial bee colony algorithm

D n G a b a A R R A A K A G B O 1 n e p f t a a t a o a a c f o h o 1 dApplied Soft Computing 12 (2012) 320–332 Contents lists available at SciVerse ScienceDirect Applied Soft Computing j ourna l ho me p age: www.elsev ier .com/ l ocate /asoc evelopment and investigation of efficient artificial bee colony algorithm for umerical function optimization uoqiang Lia,b,?, Peifeng Niua,b, Xingjun Xiaoa Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China r t i c l e i n f o rticle history: eceived 9 April 2011 eceived in revised form 11 July 2011 ccepted 14 August 2011 vailable online 22 August 2011 eywords: rtificial bee colony best-guided ABC iological-inspired optimization a b s t r a c t Artificial bee colony algorithm (ABC), which is inspired by the foraging behavior of honey bee swarm, is a biological-inspired optimization. It shows more effective than genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO). However, ABC is good at exploration but poor at exploitation, and its convergence speed is also an issue in some cases. For these insufficiencies, we propose an improved ABC algorithm called I-ABC. In I-ABC, the best-so-far solution, inertia weight and acceleration coefficients are introduced to modify the search process. Inertia weight and acceleration coefficients are defined as functions of the fitness. In addition, to further balance search processes, the modification forms of the employed bees and the onlooker ones are different in the second acceleration coefficient. Experiments show that, for most functions, the I-ABC has a faster convergence speed andptimization better performances than each of ABC and the gbest-guided

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