A Dynamic Multimodal Differential Evolution Algorithm
XiaoGang Dong, Qing Xie, Lin Ke
Available Online January 2014.
- https://doi.org/10.2991/ccit-14.2014.101How to use a DOI?
- Differential evolution, Convergence rate, Mutation Operator
- Differential evolution algorithm in solving complex function optimization problems, the problems of convergence rate and precision is not high. At the same time, there is a big difference in the performance of evolutionary algorithms for solving the different types of optimization problems. To solve above two problems, this paper proposes a dynamic multimodal differential evolution algorithm. Firstly, the dynamically population is used to improve the exploration ability of algorithms; In addition, the algorithm uses Four different types of mutation operator to Produce among individuals, choose the best among individuals to enter the next iteration , improved the algorithms's performance of solving different types of optimization problems. Through a variety of BenchMark functions to the algorithm simulation experiment, and comparing and several other classical differential evolution algorithm, show that this algorithm has better optimization performance.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - XiaoGang Dong AU - Qing Xie AU - Lin Ke PY - 2014/01 DA - 2014/01 TI - A Dynamic Multimodal Differential Evolution Algorithm BT - 2014 International Conference on Computer, Communications and Information Technology (CCIT 2014) PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/ccit-14.2014.101 DO - https://doi.org/10.2991/ccit-14.2014.101 ID - Dong2014/01 ER -