Multi-population invasive weed optimization algorithm based on chaotic sequence

doi:10.3724/SP.J.1087.2012.01958

Journal of Computer Applications ›› 2012, Vol. 32 ›› Issue (07): 1958-1961.DOI: 10.3724/SP.J.1087.2012.01958

• Artificial intelligence • Previous Articles Next Articles

Multi-population invasive weed optimization algorithm based on chaotic sequence

CHEN Huan¹,ZHOU Yong-quan¹,²,ZHAO Guang-wei¹

1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning Guangxi 530006, China
2. Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis (Guangxi University for Nationalities), Nanning Guangxi 530006, China

Received:2011-12-16 Revised:2012-02-20 Online:2012-07-05 Published:2012-07-01
Contact: ZHOU Yong-quan

基于混沌序列的多种群入侵杂草算法

陈欢¹,周永权¹,²,赵光伟¹

1. 广西民族大学信息科学与工程学院,南宁530006
2. 广西混杂计算与集成电路设计分析重点实验室(广西民族大学),南宁530006

通讯作者: 周永权
作者简介:陈欢(1988-),男,河南新乡人,硕士研究生,主要研究方向:计算智能;周永权(1962-),男,陕西旬邑人,教授,博士,主要研究方向:计算智能、神经网络;赵光伟(1986-),男,河南驻马店人,硕士研究生,主要研究方向:计算智能。
基金资助:
福建高校产学合作科技重大项目(2010H6007);智能感知与图像理解教育部重点实验室开放基金资助项目(IPIU012011001);广西民族大学研究生创新项目(gxun-chx2011078)

Abstract

Abstract: Concerning the premature convergence of invasive weed optimization algorithm, a new invasive weed optimization with multi-population based on chaotic sequence (CMIWO) was proposed. Firstly, chaotic sequence was adopted to initialize population at the initialization of algorithm, which improved the quality of the initial solution. Secondly, threshold was used to estimate the cluster degree of individuals in iterations and if cluster degree was less than threshold, initializing population with chaotic sequence was implemented again, thus the algorithm could effectively jump out of local minima. Thirdly, the weed population was divided into five groups to collaborate so as to discourage premature convergence, thus improving the algorithm's precision and increasing the convergence speed. In the end, the test results on eight test functions show that the proposed algorithm improves the accuracy by 25% to 300% than basic algorithm in terms of optimal value and 50% to 100% for standard deviation.

Key words: Invasive Weed Optimization (IWO) algorithm, chaos, multi-population, test function

摘要： 针对入侵杂草优化算法存在的早熟现象，提出一种基于混沌序列的多种群入侵杂草优化算法。首先，算法初始化时，利用混沌序列初始化种群提高初始解的质量；其次，在算法迭代过程中，若个体的聚集程度小于阈值时，再次用混沌序列重新初始化种群，使得算法迭代过程中能够有效地跳出局部极小；最后，将杂草种群分为5个种群协同合作，可有效地避免算法早熟现象，提高算法的寻优精度和收敛速度。通过对8个测试函数的测试，结果表明，所提算法获得最优值比基本入侵杂草优化算法精度提高了25％~300％；标准差提高了50％~100％。

关键词: 入侵杂草优化算法, 混沌, 多种群, 测试函数

CLC Number:

TP18

CHEN Huan ZHOU Yong-quan ZHAO Guang-wei. Multi-population invasive weed optimization algorithm based on chaotic sequence[J]. Journal of Computer Applications, 2012, 32(07): 1958-1961.

陈欢周永权赵光伟. 基于混沌序列的多种群入侵杂草算法[J]. 计算机应用, 2012, 32(07): 1958-1961.

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Multi-population invasive weed optimization algorithm based on chaotic sequence

基于混沌序列的多种群入侵杂草算法

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