Self-adaptive monarch butterfly optimization based on nonlinear cloud-transfer

被引：0
|
作者：
Li X.-P. [1 ]
Du B. [1 ]
Wang X.-W. [2 ]
机构：
[1] School of Humanities and Social Science, Chang’an University, Xi’an
[2] Faculty of Humanities and Social Science, Dalian University of Technology, Dalian
来源：
Kongzhi yu Juece/Control and Decision | 2023年 / 38卷 / 12期
关键词：
convergence accuracy; greedy strategy; grid search; monarch butterfly optimization algorithm; nonlinear cloud-transfer; self-adaptive adjustment rate;
D O I：
10.13195/j.kzyjc.2022.0448
中图分类号：
学科分类号：
摘要：
In order to solve the problem of low precision of monarch butterfly optimization (MBO)causing by diversity degradation and being easy to fall into local optimum, a self-adaptive monarch butterfly optimization based on nonlinear cloud-transfer (NCSMBO) is proposed. The evolution mechanism of the MBO is deeply explored which indicates the nature of the MBO is grid search. Nonlinear cloud-transfer operation to parent monarchs is executed utilizing a forward normal-cloud generator in both migration operators and adjusting operators, which can increase candidate solutions and improve the ability of local exploitation. Meanwhile, a greedy strategy is introduced in offspring from a cloud-transfer to enhance the feasibility. A self-adaptive adjustment rate in the form of double-circle-tangent is given to control mutation based on occurrence probability. Seven optimization algorithms including the NCSMBO are overall evaluated on the 12 benchmark functions with different features, and two types of mathematical planning problems are solved and verified. All of the simulation results show that the proposed algorithm has better convergence accuracy and stability. © 2023 Northeast University. All rights reserved.
引用
收藏
页码：3327 / 3335
页数：8
相关论文
共 24 条
  • [1] Arakawa M, Yamashita Y, Funatsu K., Genetic algorithm-basedwavelengthselectionmethodforspectral calibration, Journal of Chemometrics, 25, 1, pp. 10-19, (2011)
  • [2] Kennedy J, Eberhart R., Particle swarm optimization, Proceedings of ICNN’95 — International Conference on Neural Networks, pp. 1942-1948, (2002)
  • [3] Chen X, Meng F G, Wu J Y., Improved wolf pack algorithmfor large-scale optimization problems, Systems Engineering — Theory & Practice, 41, 3, pp. 790-808, (2021)
  • [4] Seyedali M, Andrew L., The whale optimization algorithm, Advances in Engineering Software, 95, pp. 51-67, (2016)
  • [5] Wang Y C, Liu J H, Xiao R B., Artificial bee colony algorithm based on stimulus-response labor division, Control and Decision, 37, 4, pp. 881-891, (2022)
  • [6] Wang G G, Suash D, Cui Z H., Monarch butterfly optimization, Neural Computing and Applications, 31, 7, pp. 1995-2014, (2019)
  • [7] Nalluri M R, Kannan K, Gao X Z, Et al., Multiobjective hybrid monarch butterfly optimization for imbalanced disease classification problem, International Journal of Machine Learning and Cybernetics, 11, 7, pp. 1423-1451, (2020)
  • [8] Wang G G, Hao G S, Cheng S, Et al., A discrete monarch butterfly optimization for Chinese TSP problem, International Conference on Swarm Intelligence, pp. 165-173, (2016)
  • [9] Feng Y, Yan H, Yu X, Et al., A novel monarch butterfly optimization with global position updating operator for large-scale 0-1 knapsack problems, Mathematics, 7, 11, (2019)
  • [10] Feng Y H, Wang G G, Deb S, Et al., Solving 0-1 knapsack problem by a novel binary monarch butterfly optimization, Neural Computing and Applications, 28, 7, pp. 1619-1634, (2017)
123