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Comput Intell Neurosci

Title:		A New Modified Artificial Bee Colony Algorithm with Exponential Function Adaptive Steps
Author(s):		Mao W; Lan HY; Li HR;
Address:		"Department of Mathematics, Sichuan University of Science & Engineering, Zigong, Sichuan 643000, China. Department of Mathematics, Sichuan University of Science & Engineering, Zigong, Sichuan 643000, China; Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things, Zigong, Sichuan 643000, China. School of Automation and Electronic Information, Sichuan University of Science & Engineering, Zigong, Sichuan 643000, China"
Journal Title:		Comput Intell Neurosci
Year:		2016
Volume:		20160517
Issue:
Page Number:		9820294 -
DOI:		10.1155/2016/9820294
ISSN/ISBN:		1687-5273 (Electronic) 1687-5265 (Print)
Abstract:		"As one of the most recent popular swarm intelligence techniques, artificial bee colony algorithm is poor at exploitation and has some defects such as slow search speed, poor population diversity, the stagnation in the working process, and being trapped into the local optimal solution. The purpose of this paper is to develop a new modified artificial bee colony algorithm in view of the initial population structure, subpopulation groups, step updating, and population elimination. Further, depending on opposition-based learning theory and the new modified algorithms, an improved S-type grouping method is proposed and the original way of roulette wheel selection is substituted through sensitivity-pheromone way. Then, an adaptive step with exponential functions is designed for replacing the original random step. Finally, based on the new test function versions CEC13, six benchmark functions with the dimensions D = 20 and D = 40 are chosen and applied in the experiments for analyzing and comparing the iteration speed and accuracy of the new modified algorithms. The experimental results show that the new modified algorithm has faster and more stable searching and can quickly increase poor population diversity and bring out the global optimal solutions"
Keywords:		"*Algorithms Animals *Artificial Intelligence Bees/*physiology Computer Simulation *Models, Biological *Social Behavior;"
Notes:		"MedlineMao, Wei Lan, Heng-You Li, Hao-Ru eng 2016/06/14 Comput Intell Neurosci. 2016; 2016:9820294. doi: 10.1155/2016/9820294. Epub 2016 May 17"