Real Parameter Single Objective Optimization using self-adaptive differential evolution algorithm with more strategies

Author

Brest, J. ; Boskovic, B. ; Zamuda, A. ; Fister, I. ; Mezura-Montes, Efren

Author_Institution

Inst. of Comput. Sci., Univ. of Maribor, Maribor, Slovenia

fYear

2013

fDate

20-23 June 2013

Firstpage

377

Lastpage

383

Abstract

A new differential evolution algorithm for single objective optimization is presented in this paper. The proposed algorithm uses a self-adaptation mechanism for parameter control, divides its population into more subpopulations, applies more DE strategies, promotes population diversity, and eliminates the individuals that are not changed during some generations. The experimental results obtained by our algorithm on the benchmark consisting of 25 test functions with dimensions D = 10, D = 30, and D = 50 as provided for the CEC 2013 competition and special session on Real Parameter Single Objective Optimization are presented.

Keywords

evolutionary computation; optimisation; DE strategies; parameter control; population diversity; real parameter single objective optimization; self-adaptation mechanism; self-adaptive differential evolution algorithm; subpopulation; test functions; Aging; Benchmark testing; Indexes; Optimization; Sociology; Statistics; Vectors; differential evolution; self-adaptation; single objective optimization;

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.2013.6557594

Filename

6557594