基于混沌搜索和权重学习的教与学优化算法及其应用

针对教与学优化算法容易陷入早熟收敛的问题，本研究提出了一种基于混沌搜索和权重学习的教与学优化（teaching-learning-based optimization algorithm based on chaotic search and weighted learning，TLBO-CSWL）算法。在TLBO-CSWL算法的教学阶段，不仅利用权重学习得到的个体来指引种群的进化，而且还使用正态分布随机数来替代原有的均匀随机数。另外，TLBO-CSWL还使用Logistics混沌搜索策略来提高其全局搜索能力。仿真结果表明，TLBO-CSWL的整体优化性能要好于其他所比较的算法。最后，将TLBO-CSWL用于求解非合作博弈纳什均衡问题，获得满意的结果。

Teaching-learning-based optimization algorithm based on chaotic search and weighted learning and its application

To avoid premature convergence, a teaching-learning-based optimization algorithm based on chaotic search and weighted learning (TLBO-CSWL) is introduced in this study. In the teaching phase, TLBO-CSWL does not only use the individuals obtained by weight learning to guide the population evolution, it also utilizes a normal random number to replace the original uniform random number. In addition, TLBO-CSWL uses a logistics chaotic search strategy to improve its global search ability. Simulation results showed that TLBO-CSWL outperformed other compared algorithms in terms of overall performance. Finally, the proposed algorithm was employed to solve two Nash equilibrium problems of non-cooperative game, and satisfactory results were obtained.