An Efficient Binary Differential Evolution with Parameter Adaptation

DOI

10.1080/18756891.2013.769769How to use a DOI?

Keywords

Computational Intelligence, Evolutionary Computation, Differential Evolution, Genetic Algorithm, Binary optimization

Abstract

Differential Evolution (DE) has been applied to many scientific and engineering problems for its simplicity and efficiency. However, the standard DE cannot be used in a binary search space directly. This paper proposes an adaptive binary Differential Evolution algorithm, or ABDE, that has a similar framework as the standard DE but with an improved binary mutation strategy in which the best individual participates. To further enhance the search ability, the parameters of the ABDE are slightly disturbed in an adaptive manner. Experiments have been carried out by comparing ABDE with two binary DE variants, normDE and BDE, and the most used binary search technique, GA, on a set of 13 selected benchmark functions and the classical 0-1 knapsack problem. Results show that the ABDE performs better than, or at least comparable to, the other algorithms in terms of search ability, convergence speed, and solution accuracy.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Dongli Jia
AU  - Xintao Duan
AU  - Muhammad Khurram Khan
PY  - 2013
DA  - 2013/03/01
TI  - An Efficient Binary Differential Evolution with Parameter Adaptation
JO  - International Journal of Computational Intelligence Systems
SP  - 328
EP  - 336
VL  - 6
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.769769
DO  - 10.1080/18756891.2013.769769
ID  - Jia2013
ER  -