A hybrid Grey Wolf optimizer with multi-population differential evolution for global optimization problems
Abstract
The optimization field is the process of solving an optimization problem using an optimization algorithm. Therefore, studying this research field requires to study both of optimization problems and algorithms. In this paper, a hybrid optimization algorithm based on differential evolution (DE) and grey wolf optimizer (GWO) is proposed. The proposed algorithm which is called “MDE-GWONM” is better than the original versions in terms of the balancing between exploration and exploitation. The results of implementing MDE-GWONM over nine benchmark test functions showed the performance is superior as compared to other stat of arts optimization algorithms
Full Text:
PDFDOI: http://dx.doi.org/10.21533/pen.v9i2.1818
Refbacks
- There are currently no refbacks.
Copyright (c) 2021 Nuha Sami Mohsin, Buthainah F. Abd, Rafah Shihab Alhamadani
This work is licensed under a Creative Commons Attribution 4.0 International License.
ISSN: 2303-4521
Digital Object Identifier DOI: 10.21533/pen
This work is licensed under a Creative Commons Attribution 4.0 International License