基于 Lévy 飞行的混沌教与学优化算法用于全局数值优化
Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization.
作者信息
He Xiangzhu, Huang Jida, Rao Yunqing, Gao Liang
机构信息
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; College of Electronics and Information Engineering, South-Central University for Nationalities, Wuhan 430074, China.
State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
出版信息
Comput Intell Neurosci. 2016;2016:8341275. doi: 10.1155/2016/8341275. Epub 2016 Jan 31.
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.
最近，基于教学优化算法（TLBO）作为一种新兴的受自然启发的启发式算法，受到了越来越多的关注。为了提高其收敛速度并防止其陷入局部最优，本文开发了一种新颖的元启发式算法，该算法将混沌机制和莱维飞行的特殊特性引入到TLBO的基本框架中。新算法在几个具有不同特性的大规模非线性基准函数上进行了测试，并与其他方法进行了比较。实验结果表明，所提出的算法优于其他算法，并且相对于TLBO有令人满意的改进。
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