The Cuckoo search algorithm based on fuzzy C-mean clustering

Cuckoo Search is a meta-heuristic algorithm proposed in recent years. General evaluation strategy is used in pair solution, while the quality and convergence rate of solutions decrease for the interdimensional interference when solving high-dimensional functions optimization. In order to supplement this defect, this work proposed to cluster similar components among individuals with fuzzy C-means clustering and then update the evaluation by clusters. Moreover, the new solution to the current solution can be improved by accepting with greedy strategy. The simulation results show that the improved CS can effectively increase the quality and convergence rate of solutions. Moreover, with the increase of dimensions, this advantage becomes more and more obvious. The comparison between other improved CS and meta-heuristic algorithm shows that the improved algorithm has large advantage in solving high-dimensional functions.

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