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Developing a Novel Hybrid Biogeography-Based Optimization Algorithm for Multilayer Perceptron Training under Big Data Challenge

机译:在大数据挑战下开发一种新颖的基于混合生物地理学的多层感知器训练优化算法

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摘要

A Multilayer Perceptron (MLP) is a feedforward neural network model consisting of one or more hidden layers between the input and output layers. MLPs have been successfully applied to solve a wide range of problems in the fields of neuroscience, computational linguistics, and parallel distributed processing. While MLPs are highly successful in solving problems which are not linearly separable, two of the biggest challenges in their development and application are the local-minima problem and the problem of slow convergence under big data challenge. In order to tackle these problems, this study proposes a Hybrid Chaotic Biogeography-Based Optimization (HCBBO) algorithm for training MLPs for big data analysis and processing. Four benchmark datasets areemployed to investigate the effectiveness of HCBBO in training MLPs. The accuracy of the results and the convergence of HCBBO are compared to three well-known heuristic algorithms: (a) Biogeography-Based Optimization (BBO), (b) Particle Swarm Optimization (PSO), and (c) Genetic Algorithms (GA). The experimental results show that training MLPs by using HCBBO is better than the other three heuristic learning approaches for big data processing.
机译:多层感知器(MLP)是一种前馈神经网络模型,由输入和输出层之间的一个或多个隐藏层组成。 MLP已成功应用于解决神经科学,计算语言学和并行分布式处理领域的广泛问题。尽管MLP在解决不可线性分离的问题方面非常成功,但其开发和应用中的两个最大挑战是局部最小值问题和大数据挑战下的收敛缓慢问题。为了解决这些问题,本研究提出了一种基于混合混沌生物地理的优化(HCBBO)算法,用于训练用于大数据分析和处理的MLP。利用四个基准数据集来研究HCBBO在训练MLP中的有效性。将结果的准确性和HCBBO的收敛性与三种著名的启发式算法进行了比较:(a)基于生物地理的优化(BBO),(b)粒子群优化(PSO)和(c)遗传算法(GA) 。实验结果表明,使用HCBBO训练MLP优于其他三种用于大数据处理的启发式学习方法。

著录项

  • 来源
    《Scientific programming 》 |2018年第1期| 2943290.1-2943290.7| 共7页
  • 作者单位

    Southwest Univ, Coll Comp & Informat Sci, Chongqing, Peoples R China;

    Southwest Univ, Coll Comp & Informat Sci, Chongqing, Peoples R China;

    Southwest Univ, Coll Comp & Informat Sci, Chongqing, Peoples R China;

    Southwest Univ, Coll Comp & Informat Sci, Chongqing, Peoples R China|Sichuan Univ, Coll Comp Sci, Chengdu, Sichuan, Peoples R China;

  • 收录信息 美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
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