Improved Cuckoo Search Algorithm Based on Exponential Function
The Cuckoo Search Algorithm(CS)is a novel swarm intelligence optimization algorithm inspired by biology.An improved cuckoo algorithm with adaptive adjustment of discovery probability and step size control factor is proposed to solve the problem of slow convergence speed and low calculation accuracy of CS algorithm.The algorithm uses exponential curves to simulate the changing trend of a and Pa,and establishes the dynamic adjustment model of the two parameters mentioned above,so as to effectively improve the global search ability in the initial stage of iteration and accelerate the local search in the later stage of iteration to achieve stable convergence.Finally,the cuckoo algorithm and improved cuckoo algorithm are compared and analyzed under several common benchmark functions.The experimental results show that the cuckoo algorithm of exponential curve adaptive parameter model converges faster and calculates more accurately.
Cuckoo Search Algorithm Adaptive adjustment of discovery probability Adaptive step control factor Exponential curve
Kun Wang Xiaofeng Lian Bing Pan
College of Computer and Information Engineering,Beijing Technology and Business University,Beijing,China
国际会议
江苏镇江
英文
200-207
2019-09-20（万方平台首次上网日期，不代表论文的发表时间）