A Multi-strategy Enhanced Dung Beetle Optimization Algorithm and Its Application in Engineering

Abstract

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.

Enhanced Dung Beetle Optimization Algorithm for Practical Engineering Optimization

“…Although Xun proved the superiority of the DBO algorithm, it still has shortcomings, such as easily falling into local optima and poor global search ability. Therefore, many scholars have improved the DBO accordingly [16][17][18]. Fang et al suggested a DBO algorithm that merges quantum computing with multi-strategy (QHDBO), designed to prevent the algorithm from hitting local peaks through the t-distribution mutation approach in quantum computing, enhancing its convergence speed and optimization precision [19].…”

A Dynamic Hierarchical Improved Tyrannosaurus Optimization Algorithm with Hybrid Topology Structure

“…Yet, based on the 'no free lunch' concept, it is impossible for a singular population intelligence optimization algorithm to address every optimization issue [23]. Every algorithm for optimizing population intelligence comes with its own set of limitations and constraints, leading numerous academics to suggest enhancements to these foundational algorithms [24][25][26]. These algorithms can compensate for the shortcomings of the original algorithms by integrating new strategies or the advantages of other algorithms to achieve better optimization results.…”