An Adaptive Differential Evolution Algorithm Based on New Diversity

DOI

10.1080/18756891.2013.816064How to use a DOI?

Keywords

Intelligent algorithm, Differential evolution, Population diversity, Adaptive parameter control

Abstract

A DE approach based on a new measure of population diversity and a novel parameter control mechanism is proposed with the aim of introducing a good behavior of the algorithm. The ratio of the new defined population diversity of different generations is equal to that of the population variance, therefore the adaption of parameter can use some theoretical results in. Combining with the method in, we can adjust the mutation factor and the crossover rate at each generation in the searching process. The performance of the proposed algorithm (DE-F&CR) is compared to the basic DE and other four DE algorithms over 25 standard numerical benchmarks provided by the IEEE Congress on Evolutionary Computation 2005 special session on real parameter optimization. The results and its statistical analysis show that the DE-F&CR generally outperforms the other algorithms in multi-modal optimization.

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  - Huan Lian
AU  - Yong Qin
AU  - Jing Liu
PY  - 2013
DA  - 2013/11/01
TI  - An Adaptive Differential Evolution Algorithm Based on New Diversity
JO  - International Journal of Computational Intelligence Systems
SP  - 1094
EP  - 1107
VL  - 6
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.816064
DO  - 10.1080/18756891.2013.816064
ID  - Lian2013
ER  -