电子学报 ›› 2019, Vol. 47 ›› Issue (8): 1768-1775.DOI: 10.3969/j.issn.0372-2112.2019.08.022

• 学术论文 • 上一篇    下一篇

一种矩形邻域结构的教学优化算法

何杰光, 彭志平, 林伟豪, 崔得龙   

  1. 广东石油化工学院计算机学院, 广东茂名 525000
  • 收稿日期:2018-07-10 修回日期:2019-02-22 出版日期:2019-08-25 发布日期:2019-08-25
  • 作者简介:何杰光 男,1981年生于广东茂名.博士,讲师.主要研究方向为云计算、智能优化算法和项目调度.E-mail:hubice@163.com;彭志平 男,1969年生于福建泉州.博士,教授.主要研究方向为云计算、机器学习和多agent技术.
  • 基金资助:
    国家自然科学基金(No.61772145,No.61672174);茂名市科技计划项目(No.2017287);广东石油化工学院人才引进项目(No.2016rc02).广东石油化工学院大学生创新创业训练计划项目(No.201811656057)

A Teaching-Learning-Based Optimization Algorithm with Rectangle Neighborhood Structure

HE Jie-guang, PENG Zhi-ping, LIN Wei-hao, CUI De-long   

  1. College of Computer, Guangdong University of Petrochemical Technology, Maoming, Guangdong 525000, China
  • Received:2018-07-10 Revised:2019-02-22 Online:2019-08-25 Published:2019-08-25

摘要: 为了克服原始教学优化算法在求解复杂多峰函数时全局寻优精度不高和过早收敛的缺点,提出一种矩形邻域结构和个体扰动的教学优化算法.算法将种群空间设计为矩形结构,个体的矩形邻域由矩形厚度和围绕其的矩形区域个体决定,教和学两个阶段都使用邻域最优个体引导搜索,加强了算法勘探新解和开发局部最优解的能力;为了防止算法过早陷入局部最优,增加了基于搜索边界信息引导的个体扰动阶段,使得种群即使在进化的后期仍能保持较好的多样性.对带有偏移和旋转的复杂函数进行仿真测试,结果表明新算法在求解精度和稳定性方面,在绝大多数情况下优于原始教学算法和其他一些近来的优秀改进教学算法.

关键词: 教学优化算法, 矩形邻域结构, 邻域层数, 边界信息, 个体扰动, 种群多样性

Abstract: A teaching-learning-based optimization algorithm with rectangle neighborhood structure (RNTLBO) is proposed to overcome the shortcomings of low global search precision and premature convergence of the original teaching-learning-based optimization algorithm (TLBO) while handling complex multimodal functions.In the algorithm,the population space is designed as a rectangular structure,and the individual rectangular neighborhood is determined by the rectangle thickness and the individual rectangular region surrounding it.In both teaching and learning stages,the optimal individual in the neighborhood is used to guide the search,which strengthens the ability of the algorithm to explore new solutions and exploit local optimal solutions.In order to prevent the algorithm from falling into the local optimum prematurely,the individual perturbation stage guided by search boundary information is added,so that the population can maintain good diversity even in the later evolution stage.The simulation results of complex functions with shift and rotation show that the new algorithm is superior to the original TLBO and some other recently improved variants in terms of accuracy and stability in most cases.

Key words: teaching-learning-based optimization (TLBO), rectangle neighborhood structure, layer number of neighborhood, boundary information, individual disturbance, population diversity

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