Journal
IEEE ACCESS
Volume 7, Issue -, Pages 97974-97985Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2927846
Keywords
Dandelion algorithm; Gaussian mutation; Levy mutation; probability-based mutation
Categories
Funding
- National Natural Science Foundation of China [61374148, 51775385, 71371142]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available