Abstract
Abstract The dung beetle optimization algorithm (DBO) is a novel meta-heuristic algorithm inspired by the behaviors of dung beetle populations, including rolling, dancing, foraging, breeding and stealing. As so far, the DBO algorithm has demonstrated success in addressing a wide range of complex engineering optimization problems. However, like many other meta-heuristic algorithms, it is also prone to certain limitations, such as slow convergence rates and the tendency to become trapped in local optima during the later stages of optimization. To overcome these limitations, this paper proposes a multi-strategy hybrid dung beetle optimization algorithm (MSDBO), which introduces the tangent flight strategy, golden sine search strategy, adaptive t-distribution sparrow perturbation strategy, and vertical crossover mutation strategy. To comprehensively evaluate the performance of MSDBO, simulations are conducted on 59 benchmark functions from CEC2014 and CEC2017. Experimental results demonstrate that MSDBO outperforms DBO, four advanced DBO variants, and several other popular algorithms in overall performance. Furthermore, MSDBO is employed for parameter identification in photovoltaic system models, further showcasing its effectiveness and reliability in real-world engineering applications.
Published Version
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