[1]范勤勤,柳缔西子,王筱薇,等.基于反向学习的微种群教与学优化算法及其应用[J].郑州大学学报(工学版),2020,41(04):59-67.[doi:10.13705/j.issn.1671-6833.2020.01.020]
　FAN Qinqin,LIU Dixizi,WANG Xiaowei,et al.Opposition-based Learning Teaching-learning-based Optimization Algorithm with a Micro Population and Its Application[J].Journal of Zhengzhou University (Engineering Science),2020,41(04):59-67.[doi:10.13705/j.issn.1671-6833.2020.01.020]
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基于反向学习的微种群教与学优化算法及其应用()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
41
期数:
2020年04期
页码:
59-67
栏目:
出版日期:
2020-08-12

文章信息/Info

Title:
Opposition-based Learning Teaching-learning-based Optimization Algorithm with a Micro Population and Its Application
作者:
范勤勤; 柳缔西子; 王筱薇; 韩新; 王维莉
基于反向学习的微种群教与学优化算法及其应用
Author(s):
FAN Qinqin1; 2; LIU Dixizi1; WANG Xiaowei1; HAN Xin3; WANG Weili1
1.Logistics Research Center,Shanghai Maritime University,Shanghai 201306,China;2.School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200237,China;3.Shanghai Institute of Disaster Prevention and Relief,Tongji University,Shanghai 200092,China
关键词:
教与学优化; 微种群; 反向学习; 非合作博弈
Keywords:
teaching-learning-based optimization; micro population; opposition-based learning; non-cooperative game
DOI:
10.13705/j.issn.1671-6833.2020.01.020
文献标志码:
A
摘要:
为提高教与学优化算法的收敛速率且能保证其可靠性，本文提出了一种基于反向学习的微种群教与学优化算法（Opposition-based learning Teaching-learning-based optimization algorithm with a micro population， OBL- μTLBO）。在所提算法中，利用微种群来提高教与学优化算法的收敛速率，且使用反向学习来提高算法的全局探索能力。仿真结果表明，OBL-μTLBO不仅具有较好的整体性能，而且还具有较快的收敛速度。最后，将OBL-μTLBO算法用于求解非合作博弈纳什均衡问题，取得令人满意的结果。
Abstract:
To improve the convergence speed and reliability of Teaching-learning-based optimization (TLBO) algorithm, an opposition-based learning TLBO with a micro population (OBL-μTLBO) is proposed in the current study. In the proposed algorithm, a micro population is used to speed up the convergence and an opposition-based learning is utilized to improve the global exploration capability of TLBO. Simulation results indicate that OBL-μTLBO not only has better overall performance, but also has more quick convergence speed when compared with other competitors. Finally, OBL- μTLBO is used to solve two Nash equilibrium problems of non-cooperative game, and satisfactory results are achieved.

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更新日期/Last Update: 2020-10-06