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A modified adaptive sparrow search algorithm based on chaotic reverse learning and spiral search for global optimization

  • S.I.: Applications and Techniques in Cyber Intelligence (ATCI2022)
  • Published:
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Abstract

A population-based metaheuristic algorithm that takes its cues from the foraging strategy of sparrows is called the sparrow search algorithm (SSA). While SSA is competitive when compared to other algorithms, it nevertheless has a propensity to carry out imbalanced exploitation and exploration and find the local optimum. Therefore, the modified adaptive sparrow search algorithm (MASSA), an SSA modification, is created to address these problems. To increase population variety, the MASSA uses a chaotic reverse learning technique. Second, to balance the exploitation and exploration capacities, a dynamic adaptive weight is added. In the end, an adaptive spiral search technique improves algorithm performance. Among 23 classical test functions, of which 13 are multidimensional and the other 10 are fixed dimensional, the best chaotic operator is found. It is proven that MASSA is superior. Simulation studies demonstrate that the MASSA described in this study is superior to previous algorithms in terms of stability, convergence speed, and convergence accuracy. Finally, a sample robot path planning problem is resolved using MASSA, and the experimental outcomes confirmed the viability and usefulness of MASSA.

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Data availability statement

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

The author acknowledges funding received from the following science foundations: National Natural Science Foundation of China (Grant Number 51907109).

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Authors and Affiliations

  1. School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, 255000, Shandong Province, China

    Junqi Geng, Xianming Sun & Haihua Wang

  2. State Grid Shandong Electric Power Company Zibo Power Supply Company, Zibo, 255000, Shandong Province, China

    Junqi Geng, Daohuan Liu & Fei Li

  3. State Grid Shandong Electric Power Company Zouping Power Supply Company, Binzhou, 256600, Shandong Province, China

    Xianghai Bu

  4. Beijing Institute of Precision Mechatronics and Controls, Beijing, 100081, Beijing, China

    Zengwu Zhao

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  1. Junqi Geng
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Correspondence to Xianming Sun.

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Geng, J., Sun, X., Wang, H. et al. A modified adaptive sparrow search algorithm based on chaotic reverse learning and spiral search for global optimization. Neural Comput & Applic 35, 24603–24620 (2023). https://doi.org/10.1007/s00521-023-08207-7

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  • DOI: https://doi.org/10.1007/s00521-023-08207-7

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