z-logo
open-access-imgOpen Access
Second‐order DE algorithm
Author(s) -
Zhao Xinchao,
Xu Guangzhi,
Liu Dongyue,
Zuo Xingquan
Publication year - 2017
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/trit.2017.0006
Subject(s) - order (exchange) , mathematical optimization , algorithm , operator (biology) , computer science , simple (philosophy) , mathematics , biochemistry , chemistry , philosophy , finance , repressor , epistemology , transcription factor , economics , gene
Differential evolution (DE) is a robust, efficient and simple evolutionary algorithm for various optimisation and engineering problems. It has several outstanding features such as low time complexity, ease to use and robust steadiness. So it is becoming more and more popular and is widely used in more and more applications. However, many questions are deserving to consider the critical balance between global exploration and neighbourhood exploitation. The difference vector of the mutation operator for the direction and neighbour information has not been fully exploited. Therefore, a second‐order difference vectors based DE, SODE, is proposed, which can efficiently utilise different direction information from the second‐order difference vector. The optimal second‐order difference mechanisms are proposed for DE/rand/1 and DE/best/1 to utilise the direction and neighbour information from difference vector. Then, it will guide the individuals toward the possible more encouraging areas. Extensive experiments and comprehensive comparisons show that the second‐order differenced mechanism in SODE is much better than the classical first‐order difference mechanisms based mutation strategy – ‘DE/rand/1’ and ‘DE/best/1’ as far as the converging and steady performance.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here