Shuffled frog leaping algorithm based on enhanced learning

被引：0
作者：
Zhao J. [1 ,2 ]
Hu M. [1 ,2 ]
Sun H. [1 ,2 ]
Lv L. [1 ,2 ]
机构：
[1] School of Information Engineering, Nanchang Institute of Technology, Nanchang
[2] Jiangxi Province Key Laboratory of Water Information Cooperative, Sensing and Intelligent Processing, Nanchang
来源：
Zhao, Jia (zhaojia925@163.com) | 1600年 / Inderscience Enterprises Ltd., 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland卷 / 15期
基金：
中国国家自然科学基金;
关键词：
Enhanced learning; Frog leaping rule; General centre frog; SFLA; Shuffled frog leaping algorithm;
D O I：
10.1504/IJISTA.2016.076099
中图分类号：
学科分类号：
摘要：
The paper proposes shuffled frog leaping algorithm (SFLA) based on enhanced learning, which generates a virtual general centre frog that is related to the optimal frog of each memeplex. The algorithm can utilise the superior information of each memeplex, enhance the mutual learning and use the average centre of optimal frog. In the processing of evolution, the optimal frog of sub-memeplex learns from the general centre frog and the best frog of the whole memeplex; then it enhances the learning ability of the worst frog from general centre frog. On the one hand, the evolution increases the information share and exchange among each memeplex; on the other hand, it raises the convergence velocity. The experiment results show that the new approach has better convergence speed and searching global optimum, comparing with the standard SFLA, PSO and other variants. © 2016 Inderscience Enterprises Ltd.
引用
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页码：63 / 73
页数：10
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