解连续性优化问题的摸石头过河算法

依据“摸石头过河”的思想,提出一种快速、高效的随机优化算法. 摸石头过河算法是以一个解为起点,向该起点附近邻域随机搜索若干个解,找出这些解中的最好的一个解,以此解为下次迭代的结果,然后以此点为起点,再向附近邻域随机搜索若干个解,以此类推. 解连续性优化问题时改进的方法是逐渐缩小搜索空间,对几个经典测试函数进行实验的结果表明,利用摸石头过河及其改进算法能够极大地提高收敛速度和精度.
According to idea of Wading across the Stream by feeling the way,a kind of fast efficient random optimization algorithm is put forward. The Wading across Stream Algorithm(WSA)acts a solution as a start point,then searches several solutions randomly near the start point,and finds the best solution of these solutions. This best solution is to take as next start point,and then several random solutions near this start point are searched,and so on. For solving continuous optimization problem,the improved Wading across Stream Algorithm shrinks the search space gradually. The experiment results of some classic benchmark functions show that the proposed optimization algorithms improve extraordinarily the convergence velocity and precision