求解约束优化问题的元胞混洗蛙跳算法及应用

为解决混合蛙跳算法在求解连续函数优化问题中出现的收敛速度慢、求解精度低的问题,提出一种求解约束优化问题的元胞混洗蛙跳算法.算法利用元胞的邻域结构代替基本蛙跳算法的分组方法,进而克服经典混洗蛙跳算法分组的缺点.通过元胞自动机的邻域结构和演化规则降低算法的选择压力和保持种群多样性,利用改进的螺旋进化方式和混沌变异方式平衡局部搜索和全局寻优的关系,进而提高算法寻优速度和寻优精度.经仿真实验将所提算法与5个改进蛙跳算法进行对比可知,无论是典型基准函数优化问题,还是油田措施规划方案求解产出投入比,该算法都能获得很好的求解结果.

Cellular Shuffled Frog Leaping Algorithm for Constrained Optimization and Its Application

In order to solve the problems of slow convergence speed and low precision of hybrid frog leaping algorithm for continuous function optimization problem, a cellular shuffle leapfrog algorithm is proposed. The neighborhood structure of cell is used to replace the grouping method of basic leapfrog algorithm to overcome the shortcomings of classical shuffle leapfrog algorithm. The algorithm reduces the selection pressure and maintains the population diversity through the neighborhood structure and evolution rules of cellular automata. The improved spiral evolution method and chaos mutation method are used to balance the relationship between local search and global optimization to improve the speed and accuracy of optimization. Comparing the proposed algorithm with five improved leapfrog algorithm, it can be seen that the algorithm can get good results, for 10 typical benchmark function optimization problems and oilfield measure planning scheme to solve output input ratio.