Interpolated differential evolution for global optimisation problems

被引：8

作者：

Ali, Musrrat ^{[1
]}

Pant, Millie ^{[1
]}

Nagar, Atulya K. ^{[2
]}

机构：

[1] Indian Inst Technol Roorkee, Dept Paper Technol, Roorkee 247001, Uttar Pradesh, India

[2] Liverpool Hope Univ, Dept Comp Sci, Intelligence & Distributed Syst Lab, Hope Pk Liverpool, Liverpool L16 9JD, Merseyside, England

来源：

INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS | 2010年 / 3卷 / 03期

关键词：

metaheuristics; DE; differential evolution; crossover; initial population; random numbers;

D O I：

10.1504/IJCSM.2010.037450

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Differential Evolution (DE) is a popular metaheuristics for global optimisation, but little research has been done on its initial population generation. The selection of the initial population is important, since it affects the search for several iterations and often has an influence on the final solution. In this study, quadratic interpolation is used in conjugation with pseudorandom numbers to generate initial population for DE. The proposed algorithm named Quadratic Interpolation DE (QIDE) is validated on a set of 20 benchmark problems. Numerical results show the competence of the proposed scheme in terms of convergence rate and average CPU time.

引用

页码：298 / 315

页数：18

共 28 条

[11] A numerical study of some modified differential evolution algorithms [J].

Kaelo, P ;

Ali, MM .

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2006, 169 (03) :1176-1184

[12]

Kimura S, 2005, GECCO 2005: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOLS 1 AND 2, P1341

[13] Dynamic optimization of a continuous polymer reactor using a modified differential evolution algorithm [J].

Lee, MH ;

Han, CH ;

Chang, KS .

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 1999, 38 (12) :4825-4831

[14] Optimization of low pressure chemical vapour deposition reactors using hybrid differential evolution [J].

Lu, JC ;

Wang, FS .

CANADIAN JOURNAL OF CHEMICAL ENGINEERING, 2001, 79 (02) :246-254

[15] Quasi-random initial population for genetic algorithms [J].

Maaranen, H ;

Miettinen, K ;

Mäkelä, MM .

COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2004, 47 (12) :1885-1895

[16] On initial populations of a genetic algorithm for continuous optimization problems [J].

Maaranen, Heikki ;

Miettinen, Kaisa ;

Penttinen, Antti .

JOURNAL OF GLOBAL OPTIMIZATION, 2007, 37 (03) :405-436

[17]

Noman N, 2005, GECCO 2005: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOLS 1 AND 2, P967

[18]

Pant Millie, 2008, 2008 Second UKSIM European Symposium on Computer Modeling and Simulation (EMS), P141, DOI 10.1109/EMS.2008.64

[19] Differential Evolution using Quadratic Interpolation for Initializing the Population [J].

Pant, Millie ;

Ali, Musrrat ;

Singh, V. P. .

2009 IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE, VOLS 1-3, 2009, :375-380

[20] Improved Particle Swarm Optimization with Low-Discrepancy Sequences [J].

Pant, Millie ;

Thangaraj, Radha ;

Grosan, Crina ;

Abraham, Ajith .

2008 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-8, 2008, :3011-+

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