A Improved Adaptive Cuckoo Search Algorithm Based the Population Feature and Iteration Information

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

International Journal of Communication Networks and Distributed Systems Pub Date : 2020-02-28 DOI:10.1504/IJCNDS.2020.10018198

Jia Chaochuan, Yang Ting, Wang Chuan-jiang, Fan Bing-hui, He Fugui

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引用次数: 3

Abstract

Cuckoo search (CS) is widely used to solve many optimisation problem, which is a biologically inspired the brood parasitic behaviour of a type of cuckoos and the Levy flights behaviour of some animals. However, it has been demonstrated to easily get trapped into local optimal solutions and slow convergence speed. Therefore, an improved adaptive cuckoo search (IACS) optimisation algorithm is proposed in this article. Two adaptive strategies based on the population feature and iteration information feedback which are integrated into the CS algorithm to adjust the parameters pa and α0. We compared the proposed algorithm to CS and five variants on the 30 benchmark functions proposed in CEC 2014. In addition, the proposed algorithm and CS are integrated into support vector machine (SVM) for classification. Experimental results certify that the modified algorithm is superior to the CS for most optimisation problems and has better performance than the other variants of CS algorithm.

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基于种群特征和迭代信息的改进自适应布谷鸟搜索算法

布谷鸟搜索(Cuckoo search, CS)被广泛用于解决许多优化问题，它是一种受生物学启发的布谷鸟的幼虫寄生行为和一些动物的利维飞行行为。然而，该算法容易陷入局部最优解，且收敛速度慢。为此，本文提出一种改进的自适应布谷鸟搜索(IACS)优化算法。将基于种群特征和迭代信息反馈的两种自适应策略集成到CS算法中，对参数pa和α0进行调整。我们将所提出的算法与CS以及CEC 2014中提出的30个基准函数的5个变体进行了比较。此外，将该算法与CS集成到支持向量机(SVM)中进行分类。实验结果表明，改进后的算法在大多数优化问题上都优于CS算法，且性能优于CS算法的其他变体。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Communication Networks and Distributed Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

2.50

自引率

46.20%

发文量

期刊介绍： IJCNDS aims to improve the state-of-the-art of worldwide research in communication networks and distributed systems and to address the various methodologies, tools, techniques, algorithms and results. It is not limited to networking issues in telecommunications; network problems in other application domains such as biological networks, social networks, and chemical networks will also be considered. This feature helps in promoting interdisciplinary research in these areas.