A novel dung beetle optimization algorithm based on Lévy flight and triangle walk

Abstract

The dung beetle optimization (DBO) algorithm is a meta-heuristic intelligent optimization algorithm with strong search capability and fast convergence speed. With the increasing complexity of engineering optimization problems, the DBO algorithm may get trapped in local optimal solutions during the later stage of optimization. To address this issue, this paper proposes a multi-strategy improved DBO algorithm, namely “Lévy flight triangle walk dung beetle optimization (LTDBO) algorithm”. By introducing Logistic-cubic hybrid mapping to increase the diversity of initial dung beetle populations and adopting foraging strategies based on triangle walks to enhance the randomness of the search phase and strengthen local search capabilities. In addition, we propose a Lévy flight mechanism with nonlinear weight coefficients that effectively balance local and global search capabilities and avoid getting stuck in local optimal solutions. To verify the effectiveness of the LTDBO method, a comparative experimental analysis was conducted on CEC2017 and CEC2022 test suites, comparing it with 9 classic and 5 variants optimization algorithms. The results show that the LTDBO algorithm has higher convergence accuracy and better robustness.

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