Teaching learning-based optimization (TLBO) is a popular stochastic algorithm that has recently been widely applied in a variety of optimization problems since its start. In TLBO algorithm, the concept of chaos not only shows a vital effect in its convergence but also plays a substantial role to balance of exploration and exploitation through evolution. However, TLBO is quickly trapped in local optima and premature convergence seems when applied to sophisticated complex functions. To handle these problems, we introduced an improved TLBO algorithm using chaotic concept. To achieve ability to search for exploration and exploitation, new phase called chaotic phase is added in original TLBO algorithm. The proposed method is thoroughly evaluated on benchmark test suites. The numerical result show that proposed method is relatively effective in adapting the chaotic value regarding original TLBO in terms of solution quality and convergence rate. In addition, performance of proposed method is evaluated on benchmark KDD Cup 99 intrusion dataset. The experimental results demonstrate that proposed method achieves higher predictive accuracy, detection rate, false alarm rate, and provided more significant features as compared with other wrapper techniques.

中文翻译：

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基于混沌教学学习的大规模全局优化问题算法及其应用

基于教学的优化（TLBO）是一种流行的随机算法，自问世以来，最近已广泛应用于各种优化问题。在 TLBO 算法中，混沌的概念不仅在其收敛性方面表现出至关重要的作用，而且在通过进化平衡探索和开发方面发挥着重要作用。然而，TLBO 很快陷入局部最优，当应用于复杂的复杂函数时，似乎过早收敛。为了处理这些问题，我们引入了一种使用混沌概念的改进 TLBO 算法。为了实现搜索探索和开发的能力，在原有的 TLBO 算法中增加了新的阶段称为混沌阶段。所提出的方法在基准测试套件上进行了彻底的评估。数值结果表明，所提出的方法在求解质量和收敛速度方面对原始TLBO的混沌值进行了比较有效的适应。此外，在基准 KDD Cup 99 入侵数据集上评估了所提出方法的性能。实验结果表明，与其他包装技术相比，所提出的方法实现了更高的预测准确率、检测率、误报率，并提供了更显着的特征。