Abstract: Aiming at the problem that the cultural shuffled frog leaping algorithm was easy to fall into local optimal solution when solving high-dimensional functions, we proposed a cultural shuffled frog leaping algorithm, which used the individuals in group space and belief space to complete information exchange and global optimization through reception function and influence function. Firstly, belief space individuals searched for better individuals around the superior individuals by spiral updating and random walk. Secondly, the worst individuals in group space balanced the relationship between local optimization and global exploration by learning from different knowledge, so as to improve the accuracy of optimization and speed up convergence of the algorithm. Finally, compared the proposed algorithm with 12 intelligent algorithms, the test results of typical high-dimensional benchmark function show that the algorithm has good convergence accuracy and calculation speed.

Key words: shuffled frog leaping algorithm, cultural algorithm, mutation, optimization