preprints.org > computer science and mathematics > data structures, algorithms and complexity > doi: 10.20944/preprints202402.0368.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 6 February 2024 / Approved: 6 February 2024 / Online: 6 February 2024 (10:23:28 CET)

This paper introduces a novel multi-strategy enhanced dung beetle optimization (MSDBO) algorithm that is designed to address several issues identified in the standard dung beetle optimization algorithm. Specifically, the MSDBO aims to enhance convergence speed, reduce susceptibility to local optima, and increase search accuracy. By incorporating three strategies: tent chaotic mapping for population initialization, the golden sinusoidal strategy for position updating, and the Lévy flight strategy for balancing exploration and exploitation, the standard dung beetle optimization algorithm is enhanced. The MSDBO algorithm is evaluated using twelve benchmark test functions and compared against five state-of-the-art algorithms. The results consistently show that MSDBO exhibits faster convergence speeds and more accurate solutions than the other algorithms across most of the test functions. In addition, MSDBO is also applied to optimize the parameters of a valve plate, including the close angle, cross angle, triangle groove sizes, and wrap angle. The optimization outcomes reveal that MSDBO effectively minimizes pressure ripples in the piston chamber, resulting in reduced flow rate fluctuations and noise emission compared to the initial design. This study highlights the potential of the MSDBO algorithm in tackling complex nonlinear engineering optimization problems.

Dung beetle optimization algorithm; Tent chaotic mapping; Golden sinusoidal strategy; Levy flight strategy; Numerical experiment; Engineering experiment

Computer Science and Mathematics, Data Structures, Algorithms and Complexity

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