一种混沌变异的入侵杂草优化算法及性能仿真

系统仿真学报 ›› 2016, Vol. 28 ›› Issue (8): 1732-1740.

一种混沌变异的入侵杂草优化算法及性能仿真

黄霞^1,2, 叶春明¹, 曹磊¹

1.上海理工大学管理学院,上海 200093;
2.江苏科技大学张家港校区,江苏张家港 215600

收稿日期:2015-05-19 修回日期:2015-08-25 出版日期:2016-08-08 发布日期:2020-08-17
作者简介:黄霞(1975-),女,江西九江,博士生,讲师,研究方向为智能算法,生产调度;叶春明(1964-),男,安徽宣城,博导,教授,研究方向为工业工程。
基金资助:
国家自然科学基金(71271138),上海市一流学科建设(S1201YLXK),沪江基金(A14006)

Invasive Weed Optimization Algorithm Combined with Chaotic Mutation and Analysis of Its Property

Huang Xia^1,2, Ye Chunming¹, Cao Lei¹

1. Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;
2. Zhangjiagang Campus, Jiangsu University of Science and Technology, Zhangjiagang 215600, China

Received:2015-05-19 Revised:2015-08-25 Online:2016-08-08 Published:2020-08-17

摘要/Abstract

摘要： 杂草算法是受杂草扩张繁殖行为启发而来的一种新颖的仿生群智能优化算法。在分析基本杂草算法仿生原理和局限性的基础上,提出一种改进的入侵杂草优化算法,运用混沌反向学习策略对种群进行初始化,通过变异算子增加种群的多样性;并对种群中的精英个体进行混沌搜索,以提高其跳离局部最优值的能力。对经典函数的仿真测试表明,改进算法性能优于基本杂草算法,是解决工程应用复杂函数优化问题的一种有效方法。

关键词: 杂草算法, 仿生原理, 混沌变异, 仿真测试

Abstract: Inspired by the reproductive aggressive behavior of weeds in nature, invasive weed optimization algorithm (IWO) was developed as a novel bionic swarm intelligence optimization algorithm. An improved IWO algorithm was proposed on the basis of analyzing bionic principle and limitations of basic IWO, which applied an initialization strategy based on chaotic opposition-based learning, increased the diversity of the population through the mutation operator, and enhanced its ability to jump out of local optimal value by chaotic search around current elites. Simulation results for benchmark functions show that the proposed algorithm has improved optimization property compared with IWO, as an effective method to solve complex function optimization problems in engineering application.

Key words: invasive weed algorithm, bionic principle, chaotic mutation, simulation test

中图分类号:

TP301.6

黄霞, 叶春明, 曹磊. 一种混沌变异的入侵杂草优化算法及性能仿真[J]. 系统仿真学报, 2016, 28(8): 1732-1740.

Huang Xia, Ye Chunming, Cao Lei. Invasive Weed Optimization Algorithm Combined with Chaotic Mutation and Analysis of Its Property[J]. Journal of System Simulation, 2016, 28(8): 1732-1740.

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