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Abstract: The dung beetle optimizer (DBO) is a metaheuristic algorithm with fast convergence and powerful search capabilities, which has shown excellent performance in solving various optimization problems. However, it suffers from the problems of easily falling into local optimal solutions and poor convergence accuracy when dealing with large-scale complex optimization problems. Therefore, we propose an adaptive DBO (ADBO) based on an elastic annealing mechanism to address these issues. First, the convergence factor is adjusted in a nonlinear decreasing manner to balance the requirements of global exploration and local exploitation, thus improving the convergence speed and search quality. Second, a greedy difference optimization strategy is introduced to increase population diversity, improve the global search capability, and avoid premature convergence. Finally, the elastic annealing mechanism is used to perturb the randomly selected individuals, helping the algorithm escape local optima and thereby improve solution quality and algorithm stability. The experimental results on the CEC 2017 and CEC 2022 benchmark function sets and MCNC benchmark circuits verify the effectiveness, superiority, and universality of ADBO.

一种基于弹性退火机制的自适应蜣螂优化器及其在数值问题和Reed–Muller逻辑电路优化中的应用

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