基于LLE降维思想的自然计算方法

在自然计算方法中,高维问题的出现使现有一些优化算法虽然能够避免算法陷入局部最优,但是使得算法的性能变差、运行时间变长。在传统自然计算方法的基础上,提出基于LLE ( Local Linear Embedding)算法的自然计算方法,通过对算法中邻居粒子k和维数d的取值进行分析,降维后使算法得到较好的寻优效果。在此过程中,将降维后的数据增加一个小偏置s来增加种群的多样性。将该策略分别应用于粒子群算法和遗传算法中,采用经典测试函数以及主流针对维数进行优化的4个算法来验证其性能。实验结果表明,改进的算法在求解精度和收敛速度上均有明显的提升。

Natural Computing Method Based on LLE Dimension Reduction

In the natural computing method, the appearance of high-dimensional problem can make some existing optimization algorithms avoid falling into local optimum, but it makes the performance of the algorithm worse and the running time longer. On the basis of traditional natural calculation method, a natural calculation method based on LLE(Local Linear Embedding) algorithm is proposed, which analyzes the value of neighbor particle k and dimension d, and makes the algorithm get better optimization effect after dimension reduction. In the process, a small bias s is added to the data after dimension reduction to increase the diversity of the population. The strategy is applied to PSO and GA respectively, and its performance is verified by using classical test function and four mainstream algorithms for dimension optimization. The experimental results show that the improved algorithm has obvious improvement in solving accuracy and convergence speed.