An improved osprey optimization algorithm fusing Latin hypercube and Lévy flight

Lingling Wang

doi:10.1117/12.3043184

29 August 2024 An improved osprey optimization algorithm fusing Latin hypercube and Lévy flight

Lingling Wang

Author Affiliations +

Proceedings Volume 13249, International Conference on Computer Vision, Robotics, and Automation Engineering (CRAE 2024); 132490Q (2024) https://doi.org/10.1117/12.3043184
Event: 2024 International Conference on Computer Vision, Robotics and Automation Engineering, 2024, Kunming, China

Abstract

An improved Osprey Optimization Algorithm (IOOA) fusing Latin hypercube and Lévy flight is proposed to solve the problem that the original OOA in which the initialized population diversity is low and the iterative search process is unable to jump out of local optima. First, a Latin hypercube initialization strategy is used to generate diverse population individuals. Second, a Lévy flight strategy is used to generate random steps and directions, which enables the population to jump out of the local optimum by random steps in multiple directions during the iteration process. The experimental results are numerically analyzed through simulations on four types of benchmark functions, The IOOA algorithm shows significant improvement in convergence speed and accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lingling Wang "An improved osprey optimization algorithm fusing Latin hypercube and Lévy flight", Proc. SPIE 13249, International Conference on Computer Vision, Robotics, and Automation Engineering (CRAE 2024), 132490Q (29 August 2024); https://doi.org/10.1117/12.3043184

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