东北大学学报:自然科学版 ›› 2014, Vol. 35 ›› Issue (3): 323-327.DOI: 10.12068/j.issn.1005-3026.2014.03.005

• 信息与控制 • 上一篇    下一篇

带有交叉操作的教-学优化算法

高立群,欧阳海滨,孔祥勇,刘宏志   

  1. (东北大学 信息科学与工程学院, 辽宁 沈阳110819)
  • 收稿日期:2013-06-17 修回日期:2013-06-17 出版日期:2014-03-15 发布日期:2013-11-22
  • 通讯作者: 高立群
  • 作者简介:高立群(1949-),男,辽宁沈阳人,东北大学教授,博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61273155).

TeachingLearning Based Optimization Algorithm with Crossover Operation

GAO Liqun, OUYANG Haibin, KONG Xiangyong, LIU Hongzhi   

  1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China.
  • Received:2013-06-17 Revised:2013-06-17 Online:2014-03-15 Published:2013-11-22
  • Contact: OUYANG Haibin
  • About author:-
  • Supported by:
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摘要: 针对教-学优化算法(TLBO)求解无约束数值优化问题容易陷入局部最优的不足,提出了一种带有交叉操作的教-学优化算法(C-TLBO).将差分进化算法的交叉操作引入到TLBO算法中,有效地融合了教学阶段和学习阶段,增强了算法的局部搜索,平衡了算法的开采和探索.数值结果表明该算法在优化精度、收敛速度、鲁棒性方面,优于TLBO算法、I-TLBO算法以及其他智能优化算法,具有良好的发展前景.

关键词: 教-学优化算法, 局部最优, 交叉操作, 开采, 探索

Abstract: Since the teachinglearning based optimization (TLBO) algorithm was easily trapped into local optima in solving unconstrained numerical optimization problems, a teachinglearning based optimization algorithm with crossover operation was proposed. The crossover operation of differential evolution was incorporated into TLBO, this operation was effectively integrating teaching and learning stage and it was beneficial to enhance local search and balance exploitation and exploration. The numerical results show that the proposed algorithm is better than TLBO, ITLBO and other intelligent methods in terms of optimization precision, convergence speed and robustness, and the algorithm has good perspectives.

Key words: teachinglearning based optimization algorithm, local optima, crossover operation, exploitation, exploration

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