随机交叉-自学策略改进的教与学优化算法

针对非原点最优的复杂优化问题(最优解不在坐标原点)，提出了一种基于随机交叉-自学策略的教与学优化算法(teaching and learning optimization algorithm based on random crossover-self-study strategy, CSTLBO)。对标准教与学优化算法的“教阶段”和“学阶段”的空间扰动进行了几何解释，改进了原有的“教阶段”和“学阶段”，并引入随机交叉策略和“自学”策略来提高算法的全局寻优能力。通过使用20个Benchmark函数进行仿真，并与6种改进的教与学优化算法进行结果比较及Wilcoxon秩和检验分析，结果表明CSTLBO算法能有效避免陷入局部最优，具有良好的全局搜索能力，求解精度高，稳定性好。

Teaching-learning-based optimization algorithm based on random crossover-self-learning strategy

This paper presents a teaching-learning-based optimization algorithm based on a random crossover–self-learning strategy (CSTLBO) for solving non-origin-optimal complex optimization problems in circumstances in which the optimum solution is outside the origin of the coordinates. First, the spatial perturbation in the “teaching” and “learning” stages of the standard TLBO algorithm is interpreted geometrically, and the original “teaching” and “learning” stages are improved. Then, random crossover and “self-learning” strategies are introduced to improve the global optimization ability of the algorithm. To assess the performance of the CSTLBO algorithm, 20 Benchmark functions were tested, and Wilcoxon rank sum tests were applied to the experimental results of six improved TLBO algorithms. The experimental results indicate that the CSTLBO algorithm is able to avoid premature convergence, and has the advantages in global exploration, solution quality, and stability.