多策略改进蜣螂优化算法及其应用

Multi strategy improvement of dung beetle optimization algorithm and its application

针对蜣螂优化算法全局探索能力差,容易陷入局部最优等问题,本文提出了一种基于正余弦算法和蜣螂优化算法的混合算法叫做SCDBO算法。该混合算法采用正余弦搜索算法代替蜣螂算法中滚球蜣螂的搜索机制,平衡了算法的全局搜索和局部开发能力。此外,在每次迭代过程中引入t分布扰动对蜣螂种群以一定概率进行更新的同时,引入Levy-柯西变异算子,对最优位置进行突变。不仅加快了算法的收敛速度,也减少陷入局部最优的可能。最后采用混沌映射对蜣螂种群进行初始化增强了算法的种群多样性。采用23个基准函数SCDBO算法的有效性进行了研究,实验结果表明,该算法相对于其他对比算法表现出了更好的寻优能力。为了进一步评估SCDBO算法的实际应用性能,将该算法成功应用于3个工程设计问题。通过与其他算法的比较,结果表明,SCDBO算法在解决实际工程问题方面具有很高的潜力。

Aiming at problems such as the dung beetle optimization algorithm′s poor global exploration ability and its tendency to fall into local optimization,this paper proposes a hybrid algorithm based on the positive cosine algorithm and the dung beetle optimization algorithm called the SCDBO algorithm.The hybrid algorithm adopts the positive cosine search algorithm instead of the search mechanism of the rolling dung beetle in the dung beetle algorithm,which balances the global search and local exploitation ability of the algorithm.In addition,while introducing the t-distribution perturbation to update the dung beetle population with a certain probability during each iteration,the Levy-Corsi variation operator is introduced to mutate the optimal position.This not only accelerates the convergence speed of the algorithm but also reduces the possibility of falling into the local optimum.Finally,the population diversity of the algorithm is enhanced by initializing the dung beetle population with chaotic mapping.The effectiveness of the SCDBO algorithm is investigated using 23 benchmark functions,and the experimental results show that the algorithm exhibits a better ability to find the optimum compared with other comparative algorithms.To further evaluate the performance of the SCDBO algorithm for practical applications,the algorithm was successfully applied to three engineering design problems.By comparing with other algorithms,the results show that the SCDBO algorithm has high potential in solving practical engineering problems.