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Individual Disturbance and Attraction Repulsion Strategy Enhanced Seagull Optimization for Engineering Design

Author

Listed:
  • Helong Yu

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

  • Shimeng Qiao

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

  • Ali Asghar Heidari

    (Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China)

  • Chunguang Bi

    (College of Information Technology, Jilin Agricultural University, Changchun 130118, China)

  • Huiling Chen

    (Department of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China)

Abstract

The seagull optimization algorithm (SOA) is a novel swarm intelligence algorithm proposed in recent years. The algorithm has some defects in the search process. To overcome the problem of poor convergence accuracy and easy to fall into local optimality of seagull optimization algorithm, this paper proposed a new variant SOA based on individual disturbance (ID) and attraction-repulsion (AR) strategy, called IDARSOA, which employed ID to enhance the ability to jump out of local optimum and adopted AR to increase the diversity of population and make the exploration of solution space more efficient. The effectiveness of the IDARSOA has been verified using representative comprehensive benchmark functions and six practical engineering optimization problems. The experimental results show that the proposed IDARSOA has the advantages of better convergence accuracy and a strong optimization ability than the original SOA.

Suggested Citation

  • Helong Yu & Shimeng Qiao & Ali Asghar Heidari & Chunguang Bi & Huiling Chen, 2022. "Individual Disturbance and Attraction Repulsion Strategy Enhanced Seagull Optimization for Engineering Design," Mathematics, MDPI, vol. 10(2), pages 1-35, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:2:p:276-:d:726090
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    Cited by:

    1. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Mohamed Abd Elaziz & Ahmed H. Samak, 2022. "Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer," Energies, MDPI, vol. 15(24), pages 1-14, December.

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