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一种矩形邻域结构的教学优化算法 被引量:3

A Teaching-Learning-Based Optimization Algorithm with Rectangle Neighborhood Structure
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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. 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.
作者 何杰光 彭志平 林伟豪 崔得龙 HE Jie-guang;PENG Zhi-ping;LIN Wei-hao;CUI De-long(College of Computer,Guangdong University of Petrochemical Technology,Maoming,Guangdong 525000,China)
出处 《电子学报》 EI CAS CSCD 北大核心 2019年第8期1768-1775,共8页 Acta Electronica Sinica
基金 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)
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 teaching-learning-based optimization(TLBO) rectangle neighborhood structure layer number of neighborhood boundary information individual disturbance population diversity
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  • 1俞欢军,张丽平,陈德钊,胡上序.基于反馈策略的自适应粒子群优化算法[J].浙江大学学报(工学版),2005,39(9):1286-1291. 被引量:29
  • 2吴洪,卢汉清,马颂德.基于内容图像检索中相关反馈技术的回顾[J].计算机学报,2005,28(12):1969-1979. 被引量:52
  • 3RAO R V, SAVSANI V J, VAKHARIA D P. Teachinglearning-based optimization : a novel method for constrainedmechanical design optimization problems [J] . Computer-aideddesign, 2011, 43(3) : 303-315. 被引量:1
  • 4RAO R V, SAVSANI V J, VAKHARIA D P. Teachinglearning-based optimization: an optimization method for continuousnon-linear large scale problems[J]. Information sciences,2012, 183(1) : 1-15. 被引量:1
  • 5RAO R V, PATEL V. Multi-objective optimization of heatexchangers using a modified teaching-learning-based optimizationalgorithm [J] . Applied mathematical modelling,2013, 37(3) : 1147-1162. 被引量:1
  • 6RAO R V, PATEL V. Multi-objective optimization of twostage thermoelectric cooler using a modified teaching-learning-based optimization algorithm [J] . Engineering applicationsof artificial intelligence, 2013, 26(1) : 430-445. 被引量:1
  • 7RAO R V, PATEL V. An improved teaching-learning-basedoptimization algorithm for solving unconstrained optimizationproblems[J]. Scientia iranica, 2013, 20(3) : 710-720. 被引量:1
  • 8ZOU Feng, WANG Lei, HEI Xinhong, et al. Teachinglearning-based optimization with dynamic group strategy forglobal optimization [J] . Information sciences, 2014, 273:112-131. 被引量:1
  • 9LI Jianping, BALAZS M E, PARKS G T, et al. A speciesconserving genetic algorithm for multimodal function optimization[J]. Evolutionary computation, 2002, 10(3) : 207-234. 被引量:1
  • 10LI Xiaodong. Efficient differential evolution using speciationfor multimodal function optimization[C]//Proceedings of the7th Annual Conference On Genetic and Evolutionary Computation.New York, USA, 2005: 873-880. 被引量:1

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