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期刊信息
  • 主管单位:
  • 上海市教育委员会
  • 主办单位:
  • 上海理工大学
  • 主  编:
  • 庄松林
  • 地  址:
  • 上海市军工路516号
  • 邮政编码:
  • 200093
  • 联系电话:
  • 021-55277251
  • 电子邮件:
  • xbzrb@usst.edu.cn
  • 国际标准刊号:
  • 1007-6735
  • 国内统一刊号:
  • 31-1739/T
  • 邮发代号:
  • 4-401
  • 单  价:
  • 15.00
  • 定  价:
  • 90.00
陈雪芬,叶春明.基于非线性收敛因子和标杆管理的改进教与学优化算法[J].上海理工大学学报,2022,44(5):508-518.
基于非线性收敛因子和标杆管理的改进教与学优化算法
Modified teaching-learning-based optimization algorithm based on the nonlinear convergence factor and benchmarking management
投稿时间:2021-10-03  
DOI:10.13255/j.cnki.jusst.20211003001
中文关键词:  群智能优化算法  教与学优化算法  收敛因子  标杆管理
英文关键词:swarm intelligence optimization algorithm  teaching-learning-based optimization algorithm  convergence factor  benchmarking management
基金项目:国家自然科学基金资助项目(71840003);上海市哲学社会科学一般项目(2022BGL10)
作者单位E-mail
陈雪芬 上海理工大学 管理学院上海 200093  
叶春明 上海理工大学 管理学院上海 200093 Yechm6464@163.com 
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中文摘要:
      针对教与学优化算法寻优精度低、收敛速度慢以及局部最优避免性弱的问题,提出了一种改进教与学优化算法(MTLBO)。在算法的教学和学习阶段,分别引入了非线性收敛因子调整策略和标杆管理策略。基于2种策略的随机组合形成了3种不同的MTLBOs,与标准教与学优化算法(TLBO)的对比实验结果表明,3种MTLBO均优于TLBO,其中,引入2种策略的MTLBO3取得了最佳的数值实验结果,其远优于原始TLBO。为进一步验证提出算法的有效性,与其他著名的群智能优化算法进行了数值实验对比。数值结果和收敛曲线表明,MTLBO3的寻优性能明显优于其他对比方法,具有更高的求解精度、更快的收敛速度以及更佳的局部最优避免能力。最后,使用有约束的工程优化问题进一步验证了提出算法的有效性。
英文摘要:
      A modified teaching-learning-based optimization algorithm (MTLBO) was proposed to solve the shortcomings of standard teaching-learning-based optimization algorithm (TLBO), such as low optimization accuracy, slow convergence speed and weak avoidance of local optimization. In the teaching and learning stages of the TLBO, the nonlinear convergence factor adjustment and benchmarking management strategies were introduced respectively. Based on the random combination of the two strategies, three different MTLBOs were constructed. Subsequently, the experimental results show that the three MTLBOs are better than the TLBO. Among them, the MTLBO3 with the two modified strategies achieves the best numerical results, which is much better than the original TLBO. In order to further verify the effectiveness of the proposed algorithm, numerical experiments are carried out with other well-known swarm intelligence optimization algorithms. Numerical results and convergence curves show that the optimization performance of MTLBO3 is significantly better than other comparison methods, with higher solution accuracy, faster convergence speed and better local optimization avoidance ability. Finally, the effectiveness of the proposed algorithm is further verified using constrained engineering optimization problems.
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