Improved butterfly optimization algorithm based on cosine similarity
文章出处：网络 人气：发表时间：2024-03-12 11:29
| [1] PAN W T. A new fruit fly optimization algorithm:taking the financial distress model as an example[J]. Knowledge-Based Systems,2012,26:69-74. [2] YANG X S, DEB S. Cuckoo search via Levy flights[C]//Proceedings of the 2009 World Congress on Nature and Biologically Inspired Computing. Piscataway:IEEE,2009:210-214. [3] MIRJALILI S,LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software,2016,95:51-67. [4] ARORA S,SINGH S. An effective hybrid butterfly optimization algorithm with artificial bee colony for numerical optimization[J], International Journal of Interactive Multimedia and Artificial Intelligence,2017,4(4):14-21. [5] 张达敏, 王依柔, 徐航, 等. 认知智能电网中基于能效优化的频谱分配策略[J/OL]. 控制与决策.[2021-03-31]. https://doi.org/10.13195/j.kzyjc.2019.1448. (ZHANG D M,(WANG Y Y,XU H, et al. Spectrum allocation strategy based on energy efficiency optimization in cognitive smart grid[J/OL]. Control and Decision.[2021-03-31]. https://doi.org/10.13195/j.kzyjc.2019.1448.) [6] WEN L, CAO Y. A hybrid intelligent predicting model for exploring household CO2 emissions mitigation strategies derived from butterfly optimization algorithm[J]. Science of The Total Environment,2020,727:No. 138572. [7] MALISETTI N R, PAMULA V K. Performance of quasi oppositional butterfly optimization algorithm for cluster head selection in WSNs[J]. Procedia Computer Science,2020,171:1953-1960. [8] ARORA S,SINGH S. Butterfly algorithm with Lèvy Flights for global optimization[C]//Proceedings of the 2015 International Conference on Signal Processing, Computing and Control. Piscataway:IEEE,2015:220-224. [9] ARORA S, ANAND P. Learning automata-based butterfly optimization algorithm for engineering design problems[J]. International Journal of Computational Materials Science and Engineering,2018,7(4):No. 1850021. [10] 王依柔, 张达敏, 徐航, 等. 基于自适应扰动的疯狂蝴蝶算法[J]. 计算机应用研究,2020,37(11):3276-3280.(WANG Y R,ZHANG D M,XU H,et al. Crazy butterfly algorithm based on adaptive perturbation[J]. Application Research of Computers, 2020,37(11):3276-3280.) [11] 高文欣, 刘升, 肖子雅, 等. 柯西变异和自适应权重优化的蝴蝶算法[J]. 计算机工程与应用,2020,56(15):43-50.(GAO W X,LIU S,XIAO Z Y,et al. Butterfly optimization algorithm based on Cauchy variation and adaptive weight[J]. Computer Engineering and Applications,2020,56(15):43-50.) [12] 谢聪, 封宇. 一种改进的蝴蝶优化算法[J]. 数学的实践与认识,2020,50(13):105-115.(XIE C,FENG Y. An improved butterfly optimization algorithm[J]. Journal of Mathematics in Practice and Theory,2020,50(13):105-115.) [13] 高文欣, 刘升, 肖子雅, 等. 全局优化的蝴蝶优化算法[J]. 计算机应用研究,2020,37(10):2966-2970.(GAO W X,LIU S, XIAO Z Y, et al. Butterfly optimization algorithm for global optimization[J]. Application Research of Computers,2020,37(10):2966-2970.) [14] 周晓君, 阳春华, 桂卫华. 状态转移算法原理与应用[J]. 自动化学报,2020,46(11):2260-2274.(ZHOU X J,YANG C H,GUI W H. The principle of state transition algorithm and its applications[J]. Acta Automatica Sinica,2020,46(11):2260-2274.) [15] 宋钰, 石立宝. 参数动态调整的自适应布谷鸟算法[J]. 计算机工程与应用,2020,56(23):61-67.(SONG Y,SHI L B. Dynamic parameter adjustment mechanism based self-adaptive cuckoo search algorithm[J]. Computer Engineering and Applications,2020,56(23):61-67.) [16] 何庆, 林杰, 徐航. 混合柯西变异和均匀分布的蝗虫优化算法[J]. 控制与决策,2021,36(7):1558-1568.(HE Q,LIN J,XU H. Hybrid Cauchy mutation and uniform distribution of grasshopper optimization algorithm[J]. Control and Decision, 2021,36(7):1558-1568.) [17] GHASEMI M, AKBARI E, RAHIMNEJAD A, et al. Phasor particle swarm optimization:a simple and efficient variant of PSO[J]. Soft Computing,2019,23(19):9701-9718. [18] 龙文, 伍铁斌. 协调探索和开发能力的改进灰狼优化算法[J]. 控制与决策,2017,32(10):1749-1757.(LONG W,WU T B. Improved grey wolf optimization algorithm coordinating the ability of exploration and exploitation[J]. Control and Decision,2017, 32(10):1749-1757.) [19] 陈雷, 尹钧圣. 高斯差分变异和对数惯性权重优化的鲸群算法[J]. 计算机工程与应用,2021,57(2):77-90.(CHEN L,YIN J S. Whale swarm optimization algorithm based on Gaussian difference mutation and logarithmic inertia weight[J]. Computer Engineering and Applications,2021,57(2):77-90.) [20] 刘景森, 霍宇, 李煜. 优选策略的自适应蚁狮优化算法[J]. 模式识别与人工智能,2020,33(2):121-132.(LIU J S,HUO Y,LI Y. Preferred strategy based self-adaptive ant lion optimization algorithm[J]. Pattern Recognition and Artificial Intelligence, 2020,33(2):121-132.) [21] 范千, 陈振健, 夏樟华. 一种基于折射反向学习机制与自适应控制因子的改进樽海鞘群算法[J]. 哈尔滨工业大学学报,2020, 52(10):183-191.(FAN Q,CHEN Z J,XIA Z H. A modified salp swarm algorithm based on refracted opposition-based learning mechanism and adaptive control factor[J]. Journal of Harbin Institute of Technology,2020,52(10):183-191.) [22] 王依柔, 张达敏. 融合正弦余弦和无限折叠迭代混沌映射的蝴蝶优化算法[J]. 模式识别与人工智能,2020,33(7):660-669. (WANG Y R,ZHANG D M. Butterfly optimization algorithm combining sine cosine and iterative chaotic map with infinite collapses[J]. Pattern Recognition and Artificial Intelligence, 2020,33(7):660-669.) [23] DERRAC J,GARCÍA S,MOLINA D,et al. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms[J]. Swarm and Evolutionary Computation,2011,1(1):3-18. |
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