Elite opposition-based learning multi-stage mutated invasive weed optimization algorithm

WU Cong-cong; HE Yi-chao; ZHAO Jian-li; LI Ning

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Elite opposition-based learning multi-stage mutated invasive weed optimization algorithm

更新时间：2026-09-04

- Elite opposition-based learning multi-stage mutated invasive weed optimization algorithm
- Vol. 36, Issue 3, Pages: 32-37(2019)
- 作者机构：
  
  1. 河北地质大学机构信息工程学院,河北,石家庄,050031
  2. 全北国立大学电子信息工程学院,全州,韩国,54896
- 作者简介：
- 基金信息：
- DOI：
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- Published：2019
- 稿件说明：
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吴聪聪, 贺毅朝, 赵建立, 李宁. 基于精英反向学习的阶段性变异杂草算法[J]. 微电子学与计算机, 2019, 36(3): 32-37.

WU Cong-cong, HE Yi-chao, ZHAO Jian-li, et al. Elite opposition-based learning multi-stage mutated invasive weed optimization algorithm[J]. 2019, 36(3): 32-37.
吴聪聪, 贺毅朝, 赵建立, 李宁. 基于精英反向学习的阶段性变异杂草算法[J]. 微电子学与计算机, 2019, 36(3): 32-37. DOI：

WU Cong-cong, HE Yi-chao, ZHAO Jian-li, et al. Elite opposition-based learning multi-stage mutated invasive weed optimization algorithm[J]. 2019, 36(3): 32-37. DOI：

摘要

提出了基于精英反向学习的阶段性变异杂草算法(Elite opposition-based learning mutil-stage mutated invasive weed optimization

EOMMIWO).该算法将正态分布的标准差初始值和终止值由固定设置转化为根据问题自适应产生; 在杂草进化过程中让精英个体反向学习

提高了算法的勘探能力; 另外

在算法进化的不同阶段对杂草实施不同变异

增强个体交流

提高算法的开发能力.通过8个经典的Benchmark函数测试

结果表明该算法提高了杂草算法的求解精度和收敛速度

具有很强的鲁棒性.

Abstract

An elite opposition-based learning multi-stage mutated invasive weed optimization algorithm(EOMMIWO) was proposed. First

the algorithm adaptively generated the initial value and the ending value of the standard deviation of the normal distribution according to the search area of the problem; Second

the elite individuals opposition-based learned which improved the exploration ability; Finally

different mutations were adopted in different stages the algorithm

which enhanced the communication among the individuals and improves the ability in exploitation. The test and comparison results of several complex Benchmark functions show that the proposed algorithm not only improves the solution accuracy and convergence speed of IWO algorithm

but also strengthens robustness.

关键词

Keywords

references

TIZHOOSH H R. Opposition-based learning: a new scheme for machine intelligence[C]//Proceedings of IEEE Computational Intelligence for Modelling, Control and Automation Vienna. Austria, 2005: 695-701.

BARHAGHTALAB M H, MEIGOLI V, ZAVVAR M. Optimal fuzzy controller based on chaotic invasive weed optimization for damping power system oscillation[J]. Smart Science, 2017 (2) :1-16. DOI: 1

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 (7) :1958-1961. DOI: 1958

MEHRABIAN A R, LUCAS C. A novel numerical optimization algorithm inspired from weed colonization[J]. Ecological Informatics, 2006, 1(3):355-366. DOI: 355

李森, 任晓娜.采用差分进化策略的入侵杂草改进算法及函数优化应用[J].计算机应用与软件, 2014, 31(6):285-259. DOI: 285

贺毅朝, 王熙照.差分演化的收敛性分析与算法改进[J].软件学报, 2010, 21(5): 875-885. DOI: 875

BASAK A, MAITY D D. A differential invasive weed optimization algorithm for improved global numerical optimization[J].Applied Mathematics and Computation, 2013, 219(12):6645-6668. DOI: 6645

POUYA A R, SOLIMANPUR M, REZAEE M J. Solving multi-objective portfolio optimization problem using invasive weed optimization[J]. Swarm % Evolutionary Computation, 2016(28):42-57. DOI: 42

曹磊, 叶春明.杂草－蚁群算法在应急管理中的应用[J].微电子学与计算机, 2016, 33(9):150-154. DOI: 150

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