基于自适应莱维多样性的改进蚁群算法

Improved ant colony algorithm based on adaptive Lévy diversity

本文提出了一种基于自适应莱维(Lévy)多样性机制的改进蚁群优化(SACO)算法解决算法存在收敛精度差、易陷入局部最优的问题,并将新算法应用到焊接梁工程优化问题中。SACO算法结合该机制随机步长搜索的特点提升种群多样性,使算法避免局部最优。进一步,本文设计了一系列实验测试SACO算法的性能。实验结果显示,该算法在函数实验中表现出更好的收敛性、更高的精度及更强的避免陷入局部最优的能力。最后在工程应用实验结果中,SACO算法在函数优化和焊接梁优化上展现出较强的竞争力,可作为现实工程问题求解的有效工具。

The papers proposed an improved ant colony optimization algorithm(SACO)based on the adaptive Lévy diversity mechanism to enhance convergence accuracy and the ability to avoid local optimum.The new algorithm was applied to welded beam engineering optimization problem.SACO combines the mechanism to enhance the population diversity,making the algorithm avoid local optimum.The paper designed a series of experiments to test the performance of SACO.Experimental results show that the algorithm shows better convergence,accuracy,and the ability to avoid local optimization in function experiments.Meanwhile,the proposed algorithm is applied to the welded beam design problem,obtaining significantly better results than other comparison algorithms.SACO shows competitiveness in function optimization and welded beam design optimization,which can be used as an effective tool for solving real-life engineering problems.