Genetic-Cuckoo fusion algorithm

Xie Zhang; Chengqian Zhang; Siying Wang; Zhenzhen Xi

doi:10.1117/12.2634699

4 May 2022 Genetic-Cuckoo fusion algorithm

Xie Zhang, Chengqian Zhang, Siying Wang, Zhenzhen Xi

Proceedings Volume 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021); 121720Z (2022) https://doi.org/10.1117/12.2634699
Event: International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 2021, Nanchang, China

Abstract

The genetic algorithm is an intelligent optimization algorithm derived from the law of biological evolution. Due to the weak local optimization ability of the algorithm and the increase in the number of iterations, it is easy to cause the lack of diversity of the entire population to affect the search effect. The Cuckoo search algorithm uses the Levy flight to update the position and status. The Levy flight can help the Cuckoo search algorithm avoid the shortcomings of local optimality and insufficient population diversity in the optimization process, so this paper proposes an improvement that integrates Levy operator genetic algorithm. The test function is used to test the improved genetic algorithm optimization ability and robustness and compare it with the genetic algorithm, Cuckoo search algorithm, and particle swarm optimization algorithm. The simulation results show that the algorithm has good optimization ability and robustness, and the algorithm is significantly better than the original genetic algorithm in optimization accuracy and robustness.

Citation Download Citation

Xie Zhang, Chengqian Zhang, Siying Wang, and Zhenzhen Xi "Genetic-Cuckoo fusion algorithm", Proc. SPIE 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 121720Z (4 May 2022); https://doi.org/10.1117/12.2634699

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