DocumentCode :
238837
Title :
A surrogate-assisted differential evolution algorithm with dynamic parameters selection for solving expensive optimization problems
Author :
Elsayed, Saber M. ; Ray, Tapabrata ; Sarker, Ruhul A.
Author_Institution :
Sch. of Eng. & Inf. Technol., Univ. of New South Wales, Canberra, ACT, Australia
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
1062
Lastpage :
1068
Abstract :
In this paper, a surrogate-assisted differential evolution (DE) algorithm is proposed to solve the computationally expensive optimization problems. In it, the Kriging model is used to approximate the objective function, while DE employs a mechanism to dynamically select the best performing combinations of parameters (amplification factor, crossover rate and population size). The performance of the algorithm is tested on the WCCI2014 competition on expensive single objective optimization problems. The experimental results demonstrate that the proposed algorithm has the ability to obtain good solutions.
Keywords :
evolutionary computation; statistical analysis; DE algorithm; Kriging model; dynamic parameters selection; expensive single objective optimization problems; objective function; surrogate-assisted differential evolution algorithm; Algorithm design and analysis; Computational modeling; Heuristic algorithms; Optimization; Sociology; Statistics; Vectors; Kriging model; differential evolution; parameter selection; surrogate models;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation (CEC), 2014 IEEE Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6626-4
Type :
conf
DOI :
10.1109/CEC.2014.6900351
Filename :
6900351
Link To Document :
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