A self-adaptive mutation cuckoo search algorithm
Huixian Huang, Pengfei Hu · 2016
In order to balance the exploitation and exploration, a self-adaptive mutation cuckoo search algorithm was proposed in this paper. On the one hand, the adjustment of search step according to the distance between current and optimal nests was adopt, which is beneficial to improve convergence speed. On the other hand, the self-adaptive discovery rate was introduced to increase flexibility of algorithm. Motivated by differential evolution algorithm, instead of direct replacement, the mutation and crossover operation is used when dealing with poor individual. Not only does it improve population diversity but also increase efficiency of the algorithm. When compared with other similar improved cuckoo search algorithm, the experimental results on 30 benchmark problem show that the proposed algorithm has the best performance among all existing algorithms.