Abstract: 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.

Key words: cellular automata, shuffled frog leaping algorithm, chaos, constrained optimization