融合涡流搜索和差分策略的教学优化算法

模拟课堂教学行为的教学优化算法,具有操作简单且搜索能力强的突出优点.尽管该算法为增强种群多样性采取了消除重复个体操作,然而在算法后期依然容易陷入早熟收敛.为提高教学优化算法的搜索能力,该文通过融合涡流搜索和差分进化这两种策略,提出了改进措施.改进后的算法包括:教师自学、向教师学、学生互学三种行为.首先,在每轮循环的开始...

Teaching-learning-based Optimization Integrating Vortex Search and Differential Strategy

The teaching-learning-based optimization algorithm that simulates classroom teaching behavior has the outstanding advantages of simple operation and strong search ability. Although the algorithm takes the operation of eliminating duplicate individuals to enhance the diversity of the population, it is still easy to fall into premature convergence in the later stage. In order to improve the search ability of the teaching-learning-based optimization, we propose improvement measures by integrating the two strategies of vortex search and differential evolution. The improved algorithm includes three behaviors: self-learning by teachers, learning from teachers, and mutual learning by students. First of all, at the beginning of each cycle, the teacher’s self-learning behavior based on vortex search is added, so that the teacher who is the best individual can also get a certain chance of improvement. Then, in the individual update formulas of teacher stage and student stage, the differential strategies reflecting the differences between different individuals are introduced. At the same time, the roulette selection strategy is employed in the student stage, so that the excellent individuals can get more opportunities to update. The simulation results of 10 benchmark functions show that the optimization accuracy of the proposed algorithm is not only greatly improved compared with the original algorithm, but also outperforms other comparable algorithms of the same kind for some complex functions. It is showed that it is feasible to improve the performance of teaching-learning-based optimization by integrating vortex search and differential strategies.