Dandelion algorithm with probability-based mutation

Authors

Honghao Zhu, Key Laboratory of Embedded System and Service Computing, Ministry of Education
Guanjun Liu, Key Laboratory of Embedded System and Service Computing, Ministry of Education
Mengchu Zhou, Newark College of Engineering
Yu Xie, Key Laboratory of Embedded System and Service Computing, Ministry of Education
Qi Kang, Key Laboratory of Embedded System and Service Computing, Ministry of Education

Document Type

Article

Publication Date

1-1-2019

Abstract

A dandelion algorithm (DA) is a recently-proposed intelligent optimization algorithm and shows an excellent performance in solving function optimization problems. However, like other intelligent algorithms, it converges slowly and falls into local optima easily. To overcome these two flaws, a dandelion algorithm with probability-based mutation (DAPM) is proposed in this paper. In DAPM, both Gaussian and Levy mutations can be used interchangeably according to a given probability model. In this paper, three probability models are discussed, namely linear, binomial, and exponential models. The experiments show that DAPM achieves better overall performance on standard test functions than DA.

Identifier

85070278418 (Scopus)

Publication Title

IEEE Access

External Full Text Location

https://doi.org/10.1109/ACCESS.2019.2927846

e-ISSN

21693536

First Page

97974

Last Page

97985

Volume

7

Grant

51775385

Fund Ref

National Natural Science Foundation of China

Recommended Citation

Zhu, Honghao; Liu, Guanjun; Zhou, Mengchu; Xie, Yu; and Kang, Qi, "Dandelion algorithm with probability-based mutation" (2019). Faculty Publications. 8089.
https://digitalcommons.njit.edu/fac_pubs/8089