Volume 6, Issue 6, November 2013, Pages 1094 - 1107
An Adaptive Differential Evolution Algorithm Based on New Diversity
Huan Lian, Yong Qin, Jing Liu
Received 4 September 2012, Accepted 7 April 2013, Available Online 1 November 2013.
- https://doi.org/10.1080/18756891.2013.816064How to use a DOI?
- Intelligent algorithm, Differential evolution, Population diversity, Adaptive parameter control
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Huan Lian AU - Yong Qin AU - Jing Liu PY - 2013 DA - 2013/11 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 - https://doi.org/10.1080/18756891.2013.816064 ID - Lian2013 ER -