Global-best guided fuzzy cuckoo search algorithm and its application

A global-best guided fuzzy cuckoo search algorithm is proposed to deal with the deficiencies of cuckoo search algorithm, such as poor at exploitation and accuracy, slow convergence, etc. A global-best guided strategy was introduced into the nests update formula to take advantage of the current optimal nest location information when producing new nest location in order to maintain the diversity of the nests and increase the algorithm's exploitation. In addition, the proposed method utilize fuzzy set theory to adjust the two main coefficients, one is search step, the other is the fraction of worst nests, and is thereby able to improve the accuracy and the global convergence. The performance of the proposed algorithm was tested by two classical structural reliability limited state functions and then it was applied to reliability analysis of an aircraft door locking mechanism. Experimental results show that compared with the particle swarm optimization, standard cuckoo search algorithm and improved cuckoo search algorithm, the proposed algorithm enhances the accuracy and the convergence effectively, and it has better optimization results when applied to reliability analysis problems.