A Dynamic Multimodal Differential Evolution Algorithm

DOI

10.2991/ccit-14.2014.101How to use a DOI?

Keywords

Differential evolution, Convergence rate, Mutation Operator

Abstract

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.

Copyright

© 2014, 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  - CONF
AU  - XiaoGang Dong
AU  - Qing Xie
AU  - Lin Ke
PY  - 2014/01
DA  - 2014/01
TI  - A Dynamic Multimodal Differential Evolution Algorithm
BT  - Proceedings of the 2014 International Conference on Computer, Communications and Information Technology
PB  - Atlantis Press
SP  - 395
EP  - 398
SN  - 1951-6851
UR  - https://doi.org/10.2991/ccit-14.2014.101
DO  - 10.2991/ccit-14.2014.101
ID  - Dong2014/01
ER  -