基于反向学习的微种群教与学优化算法及其应用-《郑州大学学报(工学版)》

文章信息/Info

Title:: Opposition-based Learning Teaching-learning-based Optimization Algorithm with a Micro Population and Its Application

Author(s):: Fan Qinqin1; 2; LiuDI Xizi1; Wang Xiaowei1; Han Xin3; Wang Weili1; 1. Logistics Research Center, Shanghai Maritime University, 2. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 3. Shanghai Institute of Disaster Prevention and Relief, Tongji University

Keywords:: teaching and learning optimization' target="_blank" rel="external">">teaching and learning optimization; micropopulations; reverse learning; non-cooperative games;

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.