An improved seagull optimization algorithm based on Cauchy variation and nonlinear convergence factor

Lirong Cui; Hanning Chen

doi:10.1117/12.2659634

22 November 2022 An improved seagull optimization algorithm based on Cauchy variation and nonlinear convergence factor

Lirong Cui, Hanning Chen

Proceedings Volume 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022); 124751H (2022) https://doi.org/10.1117/12.2659634
Event: Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 2022, Hulun Buir, China

Abstract

Aiming at the shortcomings of the seagull optimization algorithm in the iterative process, such as slow convergence speed and easy to fall into local optimum, an improved seagull optimization (CNSOA) algorithm based on nonlinear convergence factor and mutation using the Cauchy operator is proposed. The tent chaotic mapping strategy is used to initialize the population that make the seagull population more uniformly distributed in the search space. In the process of seagull migration, a nonlinear convergence factor is used to guide the seagull to seek optimization, so that the algorithm has better search ability. The Cauchy mutation perturbation strategy is adopted to make the algorithm better jump out of the local optimum. Finally, 9 benchmark test functions are used to test the CNSOA, and the results are compared with the SOA and 5 famous algorithms. The experimental results show that the CNSOA performs better in convergence speed and jumping out of the local optimum.

Citation Download Citation

Lirong Cui and Hanning Chen "An improved seagull optimization algorithm based on Cauchy variation and nonlinear convergence factor", Proc. SPIE 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 124751H (22 November 2022); https://doi.org/10.1117/12.2659634

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