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A Fusion Crossover Mutation Sparrow Search Algorithm

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  • Yanqiang Tang
  • Chenghai Li
  • Song Li
  • Bo Cao
  • Chen Chen

Abstract

Aiming at the inherent problems of swarm intelligence algorithm, such as falling into local extremum in early stage and low precision in later stage, this paper proposes an improved sparrow search algorithm (ISSA). Firstly, we introduce the idea of flight behavior in the bird swarm algorithm into SSA to keep the diversity of the population and reduce the probability of falling into local optimum; Secondly, we creatively introduce the idea of crossover and mutation in genetic algorithm into SSA to get better next-generation population. These two improvements not only keep the diversity of the population at all times but also make up for the defect that the sparrow search algorithm is easy to fall into local optimum at the end of the iteration. The optimization ability of the improved SSA is greatly improved.

Suggested Citation

  • Yanqiang Tang & Chenghai Li & Song Li & Bo Cao & Chen Chen, 2021. "A Fusion Crossover Mutation Sparrow Search Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-17, July.
    • Handle: RePEc:hin:jnlmpe:9952606
    DOI: 10.1155/2021/9952606
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    Cited by:

    1. Yanfeng Wang & Haohao Wang & Sanyi Li & Lidong Wang, 2022. "Survival Risk Prediction of Esophageal Cancer Based on the Kohonen Network Clustering Algorithm and Kernel Extreme Learning Machine," Mathematics, MDPI, vol. 10(9), pages 1-20, April.

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