Modified tuna swarm optimization algorithm using levy flight

Wentao Wang; Jun Tian

doi:10.1117/12.2673277

14 April 2023 Modified tuna swarm optimization algorithm using levy flight

Wentao Wang, Jun Tian

Proceedings Volume 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022); 1261318 (2023) https://doi.org/10.1117/12.2673277
Event: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 2022, Chongqing, China

Abstract

In this article, aiming at the defects of Tuna Swarm Optimization algorithm (TSO), which is easy to fall into local optimum and slow convergence speed, an improved tuna swarm optimization algorithm based on Levy flight (LTSO) is proposed. This algorithm updates the position of the optimal individual and its followers in the tuna swarm by using the characteristics of the long and short jump of Levy flight. This method can effectively enhance the global exploration effect of the algorithm and avoid the algorithm being attracted by local extreme values. In this paper, 12 benchmark functions are used to design two groups of experiments to test the performance of LTSO on high-dimensional and low-dimensional problems respectively, and then compared with TSO and other popular algorithms. It can be seen from the test results that the speed of finding the optimal value, the accuracy of the answer and the robustness of the algorithm are better than those of TSO and other algorithms. In addition, the test results of these algorithms in these benchmark functions are analyzed using Wilcoxon statistical method and Friedman method. The statistical analysis results indicate that LTSO has significant advantages over other algorithms.

Citation Download Citation

Wentao Wang and Jun Tian "Modified tuna swarm optimization algorithm using levy flight", Proc. SPIE 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 1261318 (14 April 2023); https://doi.org/10.1117/12.2673277

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