DocumentCode
618043
Title
Structured Population Size Reduction Differential Evolution with Multiple Mutation Strategies on CEC 2013 real parameter optimization
Author
Zamuda, A. ; Brest, J. ; Mezura-Montes, Efren
Author_Institution
Fac. of Electr. Eng. & Comput. Sci., Univ. of Maribor, Maribor, Slovenia
fYear
2013
fDate
20-23 June 2013
Firstpage
1925
Lastpage
1931
Abstract
This paper presents a differential evolution (DE) algorithm for real-parameter optimization. The algorithm includes the self-adaptive jDE algorithm with one of its strongest extensions, population reduction, combined with multiple mutation strategies using a structured population. The two mutation strategies used are run dependent on the population size, which is reduced with growing function evaluation number. The population is structured with a separate part where only DE/best strategy is executed and then the best vectors are exchanged with the main population part. Algorithm performance assessment results are presented for 10, 30, and 50 dimension settings for all of the 28 problems included in the Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session and Competition on Real-Parameter Optimization.
Keywords
evolutionary computation; CEC 2013 real parameter optimization; DE algorithm; growing function evaluation number; multiple mutation strategy; self-adaptive jDE algorithm; structured population size reduction differential evolution; Educational institutions; Evolutionary computation; Indexes; Optimization; Sociology; Statistics; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation (CEC), 2013 IEEE Congress on
Conference_Location
Cancun
Print_ISBN
978-1-4799-0453-2
Electronic_ISBN
978-1-4799-0452-5
Type
conf
DOI
10.1109/CEC.2013.6557794
Filename
6557794
Link To Document