河北师范大学学报—

期刊信息

刊名：河北师范大学学报（自然科学版）Journal of Hebei Normal University (Natural Science)
主办：河北师范大学
ISSN： 1000-5854
CN： 13-1061/N
中国科技核心期刊
中国期刊方阵入选期刊
中国高校优秀科技期刊
华北优秀期刊
河北省优秀科技期刊

协同采编系统

友情链接

基于单增量和全局维度学习策略的萤火虫算法

（北京邮电大学理学院，北京 100876）
DOI： 10.13763/j.cnki.jhebnu.nse.202301006

Firefly Algorithm Based on Single Increment and Global Dimension Learning Strategy

GE Wanzhen,ZHAO Xinchao,LI Zixu,WU Lingyu

PDF
下载
HTML

摘要/Abstract

摘要：

萤火虫算法的搜索过程较依赖于最优萤火虫，而最优萤火虫并不进行有导向的寻优移动，算法易陷入局部最优. 为此，提出了一种基于单增量和全局维度学习策略的萤火虫算法. 在萤火虫个体移动时，该算法并不叠加萤火虫个体的当前位置，而是将累加的位置增量作为新的搜索方向，用于更新萤火虫的位置. 该算法大大降低了萤火虫当前位置对搜索过程的影响，有利于算法更快的跳出当前局部最优，进行更大范围的寻优；其次，对最优萤火虫进行一定次数的单维度学习，将学习后的萤火虫引导种群进化. 在基准测试函数上的实验结果表明，该算法优于其他几种改进的群智能优化算法，具有良好的跳出局部最优的能力.

Abstract：

The firefly algorithm′s search process is more reliant on the optimal firefly,which does not move in a guided search,making it easy for the algorithm to fall into the local optimum. For which this paper proposes a firefly algorithm based on single incremental and global dimensional learning strategy. When fireflies move individually,the algorithm we proposed does not superimpose their current positions,but instead uses the accumulated position increments as a new search direction to update their positions. This algorithm we proposed greatly reduces the influence of the current positions of fireflies on the search process,allows the algorithm to jump out of the current local optimum faster and searches a larger range for the optimum. Besides,a certain number of single-dimensional learning is applied to the best fireflies,and the learned fireflies guide population evolution. The algorithm proposed in this paper outperforms other upgraded swarm intelligence algorithms and has a good ability to jump out of local optimums,according to experimental results on benchmark test functions.

关键词

关键词： 萤火虫算法；全局最优萤火虫；寻优移动；单维度学习；单增量策略

Key words： firefly algorithm ; global optimal firefly ; optimization movement ; single dimension learning ; single increment strategy

参考文献 18

[1] 雷秀娟. 群智能优化算法及其应用[M]. 北京:科学出版社,2012. LEI Xiujuan. Swarm Intelligent Optimization Algorithms and Their Applications[M]. Beijing:Science Press,2012.
[2] DAN S. Evolutionary Optimization Algorithms:Biologically Inspired and Population-based Approaches to Computer Intelligence[M]. Beijing:Tsinghua University Press,2018.
[3] KENNEDY J,EBERHART R. Particle Swarm Optimization[J]. Proceedings International Conference on Neural Networks,1995(4):1942-1948. doi:10. 1007/978-0-387-30164-8_630
[4] HAN H G,BAI X,HAN H Y,et al. Self-adjusting Multi-task Particle Swarm Optimization[J]. IEEE Transactions on Evolutionary Computation,2022,26(1):145-158. doi:10. 1109/TEVC. 2021. 3098523
[5] STORN R，PRICE K. Differential Evolution a Simple and Efficient Heuristic for Global Optimization over Continuous Spaces[J]. Journal of Global Optimization,1997,11(4):341-359. doi:10. 1023/A:1008202821328
[6] 谭营. 烟花算法引论[M]. 北京：科学出版社,2015. TAN Ying. Introduction to Fireworks Algorithm[M]. Beijing:Science Press,2015.
[7] YANG X S. Firefly Algorithms for Multimodal Optimization[J]. Stochastic Algorithms:Foun Ations and Applications,2009,5792:169-178. doi:10. 1007/978-3-642-04944-6_14
[8] 王艳丽,梁静,薛冰,等. 基于进化计算的特征选择方法研究概述[J]. 郑州大学学报(工学版),2020,41(1):49-57. doi:10. 13705/j. issn. 1671-6833. 2019. 04. 026 WANG Yanli,LIANG Jing,XUE Bing,et al. Research on Evolutionary Computation for Feature Selection[J]. Journal of Zhengzhou University (Engineering Science),2020,41(1):49-57.
[9] 臧睿,李辉辉. 基于标准萤火虫算法的改进与仿真应用[J]. 计算机科学,2016，43(S2):113-116. ZANG Rui,LI Huihui. Improvement and Simulation Application Based on Standard Firefly Algorithm[J]. Computer Science,2016,43(S2):113-116.
[10] YU S H,ZUO X K,FAN X L,et al. An Improved Firefly Algorithm Based on Personalized Step Strategy[J]. Computing,2021,103(4):735-748. doi:10. 1007/s00607-021-00919-9
[11] 赵嘉，陈文平,肖人彬,等. 面向多峰优化问题的自主学习萤火虫算法[J]. 控制与决策,2022,37(8):1971-1980. doi:10. 13195/j. kzyjc. 2020. 1812 ZHAO Jia,CHEN Wenping,XIAO Renbin,et al. Firefly Algorithm Based on Self-learning for Multi-peak Optimization Problem[J]. Control and Decision,2022,37(8):1971-1980.
[12] 赵嘉,谢智峰,吕莉,等. 深度学习萤火虫算法[J]. 电子学报,2018，46(11):2633-2641. doi:10. 3969/j. issn. 0372-2112. 2018. 11. 010 ZHAO Jia,XIE Zhifeng,LV Li,et al. Firefly Algorithm with Deep Learning[J]. Acta Electronica Sinica,2018,46(11):2633-2641.
[13] YANG X S. Firefly Algorithm,Levy Flights and Global Optimization[C]∥29th SGAI International Conference on Innovative Technigues and Application of Artificial Intelligence. Cambridge:Springer,2010:209-218. doi:10. 1007/978-1-84882-983-1_15
[14] WU J R,WANG Y G,BURRAGE K,et al. An Improved Firefly Algorithm for Global Continuous Optimization Problems[J]. Expert Systems with Applications,2020,149:113340. doi:10. 1016/j. eswa. 2020. 113340
[15] TIAN Y F,GAO W M,Yan S. An Improved Inertia Weight Firefly Optimization Algorithm and Application[C]∥International Conference on Control Engineering and Communication Technology,Shenyang，2012:64-68. doi:10. 1109/ICCECT. 2012. 38
[16] KENNEDY J. Bare Bones Particle Swarms[C]∥Proceedings of the 2003 Swarm Intelligence Symposium. Indianapolis:IEEE,2003:80-87. doi:10. 1109/SIS. 2003. 1202251
[17] LIANG J J,QU B Y,SUGANTHAN P N. Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-parameter Numerical Optimization[R]. Singapore：Nanyang Technological University,2014.
[18] ZERVOUDAKIS K,TSAFARAKIS S,PARASKEVI-PANAGIOTA S. A New Hybrid Firefly-genetic Algorithm for the Optimal Product Line Design Problem[C]∥International Conference on Learning and Intelligent Optimization. Cham：Springer,2020:284-297. doi:10. 1007/978-3-030- 38629-0_23