Improvement cuckoo search algorithm for function optimization problems
Xinxi Hu · Jisuanji gongcheng yu sheji · 2013
To improve the refining ability and convergence rate of cuckoo search algorithm for function optimization problems,an improved algorithm based on self-adaptive machine is proposed.The self-adaptive machine is used to control the scaling factor and find probability so as to improve population diversity and avoid premature,as a result,more individuals participating in the evolution,and then refining ability and convergence rate are improved.The results of experiment show the improved algorithm based on self-adaptive scaling factor(rCS),and the improved algorithm based on self-adaptive finding probability(paCS)make great improvement on refining ability and convergence rate,comparing with the standard cuckoo search algorithm.They also suggest that the improved algorithm iCS based both on rCS and paCS has better refining ability and convergence rate than rCS and paCS.