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. 2016:2016:8341275.
doi: 10.1155/2016/8341275. Epub 2016 Jan 31.

Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization

Xiangzhu He  1 , Jida Huang  2 , Yunqing Rao  2 , Liang Gao  2
Affiliations

Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization

Xiangzhu He et al. Comput Intell Neurosci. 2016.

Abstract

Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO.

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Figures

Figure 1
Figure 1
Convergence curve of six functions.
Figure 2
Figure 2
Convergence curve of four functions.
Pseudocode 1
Pseudocode 1
Pseudocode of CTLBO.
See this image and copyright information in PMC

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