双种群协同演化的改进蜜獾算法.

To address the weaknesses of the honey badger algorithm, specifically its limited capacity for local search and susceptibility to local optima, this paper proposed an enhanced version based on coevolution with two populations. This approach used Cubic chaotic mapping to initialize the population, thereby expanding the search space and improving its distribution. Moreover, it introduced a dual-population optimization mechanism that combined the slime mold algorithm with the honey badger algorithm. By leveraging the strengths of both methods, the individuals could more effectively hone-in on the target location, resulting in improved search efficiency and optimization performance. To further improve the algorithm's ability to escape local optima, it employed a Cauchy random reverse perturbation strategy to disturb the optimal position of the honey badger population. By means of the experiment of improving the effectiveness of a single strategy, different high-dimensional experiments with seven other algorithms and Wilcoxon rank-sum tests, experimental results demonstrate that the proposed algorithm has high convergence accuracy and fast solving times. Finally, this paper applied the improved algorithm to the design of compression springs and pressure vessels, which further confirmed the efficacy of improved strategy and the practical utility of the algorithm. [ABSTRACT FROM AUTHOR]