NDLI: A differential evolution algorithm with variable parameter search for real-parameter continuous function optimization

Content Provider	IEEE Xplore Digital Library
Author	Tasgetiren, M.F. Quan-Ke Pan Suganthan, P.N. Yun-Chia Liang
Copyright Year	2009
Description	Author affiliation: Department of Operations Management at Sulatan Qaboos University, Muscat, Oman (Tasgetiren, M.F.) \|\| Department of Industrial Engineering and Management, Yuan Ze University, Taiwan, R.O.C. (Yun-Chia Liang) \|\| School of Computer Science, Liaocheng University, China (Quan-Ke Pan) \|\| School of Electrical and Electronics Engineering at Nanyang Technological University 639798, Singapore (Suganthan, P.N.)
Abstract	This paper presents a novel differential evolution algorithm based on variable parameter search to solve realparameter continuous function optimization problems. In order to provide differential evolution algorithm with local intensification capability, each trial individual is generated by a variable parameter search procedure using variable mutation scale factor and crossover rate as well as (possibly) variable mutation strategies. The novelty stems from the fact that while a pure differential evolution algorithm achieves global exploration during the search process, variable parameter search procedure intensifies the search around local minima by using traditional DE mutation and crossover operators as well as variable mutation strategies. The algorithm was tested using benchmark instances designed for a special session in CEC05 and other instaces from the literature. The experimental results show its highly competitive performance against the very recent differential evolution algorithm with local search by Noman and Iba in [1] (IEEE Transaction on Evolutionary Computation, Vol. 12, No. 1, pp. 107-125, February 2008).
Starting Page	1247
Ending Page	1254
File Size	308857
Page Count	8
File Format	PDF
ISBN	9781424429585
DOI	10.1109/CEC.2009.4983088
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-18
Publisher Place	Norway
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Genetic mutations Stochastic processes Optimization methods Biological cells Polynomials Benchmark testing Algorithm design and analysis Evolutionary computation Genetic algorithms Particle swarm optimization
Content Type	Text
Resource Type	Article

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