Improved cuckoo search algorithm and its application to permutation flow shop scheduling problem

• Published in: Journal of algorithms & computational technology 2020-09, Vol.14
• Main Authors: Zhang, Lieping, Yu, Yanlin, Luo, Yingxiong, Zhang, Shenglan
• Format: Article
• Language: English
• Subjects: Adaptive algorithms; Adaptive search techniques; Algorithms; Job shop scheduling; Job shops; Mathematical analysis; Parameters; Permutations; Search algorithms; Search process
• ISSN: 1748-3026; 1748-3018; 1748-3026
• DOI: 10.1177/1748302620962403

Aiming at the problem that the standard cuckoo search algorithm relies on Levy flights, which leads to the step-size randomness of the search process, a self-adaptive step cuckoo search algorithm based on dynamic balance factor is proposed in our paper. First, two parameters are introduced in our paper, which were iteration number ratio parameter and adaptability ratio parameter. Then, a dynamic balance factor parameter is introduced to adjust the weight number of iteration number ratio parameter and adaptability ratio parameter. Finally, parameter skewness value calculation method and self-adaptive step strategy were proposed combined with the dynamic balance factor. Six typical test functions are used to test the performance of the proposed algorithm, the standard cuckoo search algorithm and the self-adaptive step cuckoo search algorithm which relies only on the iterative times. The test results show that the proposed algorithm had good convergence speed and accuracy. Meanwhile, taking the permutation flow shop scheduling problem as an example, eight operators of Car benchmark class are used as the test data to compare the performance of three algorithms, the effectiveness and superiority of the algorithm in solving the permutation flow shop scheduling problem are verified.