Academic Journal of Computing & Information Science, 2022, 5(6); doi: 10.25236/AJCIS.2022.050602.
Xinyu Li1, Yingping Su2
1School of The First Clinical Medical College, Shanxi Medical University, 030001, Taiyuan, China
2School of Business, Southwest University, 402460, Chongqing, China
Based on the defect that whale optimization algorithm is easy to fall into local optimization and slow convergence speed, this paper proposes an improved whale optimization algorithm based on Gauss mutation and differential evolution. The algorithm initializes the population through logistic chaos, improves the diversity and randomness of the population, and enhances the global search ability of the algorithm. Gaussian mutation and differential evolution are introduced to enhance the local search ability and improve the search accuracy. The performance of the improved optimization algorithm is tested by 12 benchmark functions. The results show that compared with the basic whale algorithm, particle swarm optimization algorithm and Multi-Verse Optimizer algorithm, IWOA algorithm can improve the convergence speed of the population and improve the search accuracy and stability of the algorithm to a certain extent.
Chaos initialization, Gaussian mutation, Differential evolution, Benchmark functions
Xinyu Li, Yingping Su. Whale optimization algorithm based on Gaussian mutation and differential evolution. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 6: 7-13. https://doi.org/10.25236/AJCIS.2022.050602.
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