Research and Application of Multi Sub-population Invasive Weed Optimization Algorithm

doi:10.3969/j.issn.1000-3428.2014.02.040

Abstract

Abstract: Aiming at the problems of standard Invasive Weed Optimization(IWO) algorithm such as population diversity declining in the late evolution and easily trapping into local extremum, an improved algorithm, Multi Sub-population Invasive Weed Optimization algorithm based on K-means clustering(K-MSIWO), is proposed. In K-MSIWO, the weed population is divided into three sub-populations by using K-means clustering. The co-evolutionary relationship of individual and individual, sub-population and sub-population is established through intraspecific and interspecific competition to increase the diversity of the weed population. When the convergence velocity of algorithm begins falling, the random perturbation mutation strategy is adopted for the premature individual to help them out of local minimum. Experimental results on several benchmark functions show that the modified algorithm is applied to PID control parameter tuning of second-order and high-order system, the overshoots of the systems are reduced by 33.2% and 50% respectively compared with GA approaches, and K-MSIWO has better accuracy and consistency.

Key words: Invasive Weed Optimization(IWO), K-means clustering, multi sub-population, competition, mutation, PID controller

摘要： 针对入侵杂草优化算法(IWO)进化后期种群多样性、优势个体易陷入局部极值的问题，提出一种基于K-均值聚类的多子群入侵杂草优化算法(K-MSIWO)。该算法利用K-均值聚类算法将杂草种群分为3个子群，通过种内和种间竞争策略建立个体之间、子群之间的协同进化关系，提高杂草种群的多样性。当算法的收敛速度下降时，对种群中早熟的个体采用随机扰动的变异策略，帮助其跳出局部极值。基准函数测试结果表明，将该算法用于二阶和高阶系统的PID控制器参数整定，与遗传算法的整定结果相比，系统超调量分别下降33.2%和50%，具有较好的寻优精度和一致性。

关键词: 入侵杂草优化, K-均值聚类, 多子群, 竞争, 变异, PID控制器

CLC Number:

TP18

ZUO Xu-kun, SU Shou-bao. Research and Application of Multi Sub-population Invasive Weed Optimization Algorithm[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2014.02.040.

左旭坤，苏守宝. 多子群入侵杂草优化算法研究及应用[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2014.02.040.

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References

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