Cuckoo Search Algorithm with Dynamic Step and Discovery Probability

doi:10.16182/j.issn1004731x.joss.17-9093

Abstract

Abstract: In order to further improve the low accuracy and slow convergence speed of algorithm search, a cuckoo search algorithm with dynamic step size and probability of discovery is proposed. The algorithm dynamically constrains the Levy's moving step of each generation by introducing the step adjustment factor, which makes the Levy's flight mechanism adaptive. In the probability of finding, the random inertia weight with uniform distribution and F distribution is used to change the fixed value of the probability of discovery, to strengthen the diversity of the population and to keep the balance between global search and local exploration. The experiment result proves that the proposed algorithm has a good feasibility, and the optimization results and the convergence speed of the algorithm increase.

Key words: cuckoo search algorithm, step adjustment factor, levy flight, adaptive, random inertia weight

CLC Number:

TP301.6

Liu Jingsen, Liu Xiaozhen, Li Yu. Cuckoo Search Algorithm with Dynamic Step and Discovery Probability[J]. Journal of System Simulation, 2020, 32(2): 289-298.

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