A Multi-Strategy Enhanced Dung Beetle Optimization Algorithm for Global Optimization

Algorithms are fundamental to solving complex problems in science and engineering. However, conventional methods often struggle with nonlinear, high-dimensional landscapes. The biologically inspired dung beetle optimization algorithm has offered promising solutions but is still prone to premature convergence and falling into local optima. This article presents a multi-strategy enhanced dung beetle optimization algorithm that integrated tent chaotic mapping for population initialization, a golden sine strategy for position updating, Lévy flights to escape local minima, and dynamic weighting coefficients for adaptive search balancing. These strategies collectively enhanced population diversity and improved the balance between exploration and exploitation. Benchmarking on the CEC2017 test suite and real-world engineering problems demonstrated that the multi-strategy enhanced dung beetle optimization algorithm achieved superior convergence speed, solution accuracy, and robustness when compared with the standard dung beetle optimization algorithm and other state-of-the-art metaheuristics.