Quantum Chicken Swarm Optimization with Levy Flight and Its Application in Parameter optimization of Random Forest

Zhang, Jiangnan; Xia, Kewen; Yin, Zhixian

doi:10.1109/eitce47263.2019.9094981

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…Levy flight and random forest were introduced by Zhang et al [27] to solve the problem of low accuracy and it optimize the radio frequency. Levy flight and non-linear decline strategy were introduced by Liang et al [28] to improve the convergence accuracy and stability of the algorithm.…”

Section: Related Workmentioning

confidence: 99%

An Improved Chicken Swarm Optimization Algorithm Based on Adaptive Mutation Learning Strategy

Zhou¹,

Zhou²,

Gao³

2022

Journal of Compurters

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<p>To solve the problem that the Chicken swarm optimization (CSO) has low solution accuracy and tends to fall into the local optimum on later stages of iteration, an adaptive mutation learning Chicken swarm optimization (AMLCSO) is proposed in this paper. Firstly, to solve the problem of uneven initial distribution and improve the algorithm’s stability, a good-point set is introduced. Secondly, according to the difference between the current individual position and the optimal individual position, the nonlinear adaptive adjustment of weight is realized and the position update step is dynamically adjusted. This strategy improves the algorithm’s convergence. Thirdly, the learning update strategies of Gaussian mutation and normal distribution are introduced to improve the probability of selection and solving accuracy and avoid falling into the local optimum. Finally, the AMLCSO is compared with other standard algorithms and improved Chicken swarm optimization algorithms on twenty benchmark test functions. The experimental results show the AMLCSO has faster convergence and higher solution accuracy.</p> <p> </p>

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Section: Related Workmentioning

confidence: 99%