An Improved Optimization Dung Beetle Algorithm

The dung beetle optimization technique has the issue of arbitrarily initializing the population while solving complex problems, leading to unstable search results and limited global search capability. This article suggests three-improvement version of the dung beetle optimization method in light of this. First, this study employs the TENT chaotic map with strong ergodicity to generate the initial population and uses the Reverse learning technique in order to increase the randomness of the population and obtain a suitable initial solution position. Second, this paper uses the Levy flight strategy to optimize the algorithm weight and the Levy flight improvement weight strategy to maintain a high global development ability in the algorithm optimization due to the original algorithm's weak ability to jump out of local optimization and the lack of randomness. Finally, in order to increase the optimization speed of the algorithm, a new address update method is proposed to address the drawbacks of the original algorithm's address update approach. The improved optimization algorithm incorporates both local exploration and global optimization characteristics after 10 benchmark function tests and comparison with other algorithms. This enhances the algorithm's capacity to jump out of local optima and gives it good robustness and optimization accuracy.