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Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms
- Source :
- IEEE Access, Vol 9, Pp 77416-77437 (2021)
- Publication Year :
- 2021
- Publisher :
- IEEE, 2021.
-
Abstract
- As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems. As a kind of local search, chaotic local search (CLS) has a strong ability to get rid of the local optimum due to its integration of chaotic maps. To enhance GWO, CLS is always incorporated into GWO to increase its population diversity and accelerate algorithm’s convergence. However, it is still unclear that how may chaotic maps should be used in CLS and how to embed them into GWO. To address these challenging issues, this paper studies both single and multiple chaotic maps incorporated GWOs. Extensive comparative experiments are conducted based on IEEE Congress on Evolutionary Computation (CEC) benchmark test suit. The results show that CLS incorporated GWOs generally perform better than the original GWO, suggesting the effectiveness of such hybridization. Moreover, a remarkable finding of this work is that the piecewise linear chaotic map (PWLCM) and Gaussian map have the most potential to improve the search performance of GWO. Additionally, CLS incorporated GWOs also perform significantly better than some other state-of-the-art meta-heuristic algorithms. This study not only gives more insights into the mechanism of how CLS makes influence on GWO, but also finds that the most suitable choice of chaotic map for it.
- Subjects :
- 0209 industrial biotechnology
General Computer Science
Chaotic
soft computing
02 engineering and technology
grey wolf optimizer
Piecewise linear function
020901 industrial engineering & automation
Local optimum
Convergence (routing)
0202 electrical engineering, electronic engineering, information engineering
Memetics
General Materials Science
Local search (optimization)
Electrical and Electronic Engineering
Computational intelligence
business.industry
IEEE Congress on Evolutionary Computation
General Engineering
chaotic local search
TK1-9971
meta-heuristics
Benchmark (computing)
020201 artificial intelligence & image processing
optimization algorithms
Electrical engineering. Electronics. Nuclear engineering
business
Algorithm
Subjects
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 9
- Database :
- OpenAIRE
- Journal :
- IEEE Access
- Accession number :
- edsair.doi.dedup.....27c2a3643d486c7adf5fccfb0e347d7a