The existing Dung Beetle Optimization(DBO)algorithm has the disadvantages of poor search accuracy and insufficient global search ability,thereby easily falling into local optima.This paper proposes a multi-strategy improved dung beetle optimization algorithm that uses a chaotic opposition-based learning strategy to initialize the dung beetle population,whereby dung beetle individuals are evenly distributed in solution space and population diversity is improved.The golden sine strategy with a nonlinear weight is introduced to improve the ball-rolling behavior and coordinate the global search and local mining ability of the algorithm.Foraging behavior is improved by referring to the position update strategy of the sparrow search algorithm,which brings the population close to the optimal position and improves convergence speed and algorithmic accuracy.Stealing behavior is improved by introducing a piecewise function,which benefits the population in the full global exploration in the early iteration stages,to avoid premature convergence of the algorithm.The Cauchy-Gaussian mutation strategy with a nonlinear weight is used to randomly perturb the current optimal position and guide the algorithm to jump out of the local optimal position.The proposed algorithm is compared with five optimization algorithms using 23 benchmark functions,12 CEC2022 test functions,and two engineering optimization problems.The experimental results show that the proposed algorithm is superior to the other algorithms and ranks first among at least 21 benchmark functions,10 CEC2022 test functions,and two engineering optimization problems.Compared with the original dung beetle optimization algorithm,the proposed algorithm exhibits significant improvements in convergence accuracy,convergence speed,global search ability,and stability.
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