计算机工程与应用 ›› 2015, Vol. 51 ›› Issue (24): 12-17.

• 博士论坛 • 上一篇    下一篇

改进的双种群竞争教与学优化算法

王培崇1,2,钱  旭2   

  1. 1.石家庄经济学院 信息工程学院,石家庄 050031
    2.中国矿业大学 机电与信息工程学院,北京 100083
  • 出版日期:2015-12-15 发布日期:2015-12-30

Improved teaching  learning based optimization with double populations competition

WANG Peichong1,2, QIAN Xu2   

  1. 1.School of Information Engineering, Shijiazhuang University of Economics, Shijiazhuang 050031, China
    2.School of Mechanical Electronic & Information Engineering, China University of Mining & Technology, Beijing 100083, China
  • Online:2015-12-15 Published:2015-12-30

摘要: 为了克服教与学优化算法在求解高维函数问题时,容易早熟,收敛速度慢,解精度低的弱点,提出一种引入竞争机制的双种群教与学优化算法。在该算法中设置两个教师,并基于帝国竞争优化机制将种群初始化成为两个学生种群,每一个教师带领自己的种群独立进化。在进化过程中,教师可以利用自己的影响力将外种群内的成员吸收进入自己的种群。为了提高教师个体的学习能力,引入反向学习机制。在多个Benchmark函数的测试表明,改进算法解精度较高,全局收敛能力强,适合求解较高维度的函数优化问题。

关键词: 教与学优化, 早熟, 双种群, 竞争, 反向学习

Abstract: To overcome these weakness of premature, low solution precision and slow convergence speed in solving higher dimension functions, an improved Teaching Learning Based Optimization(TLBO) algorithm with double populations competition is proposed. Two teachers with the best achievement individual are chosen from population and other individuals are divided into two student populations by imperialist competitive. Each teacher guides itself student population. In the iteration, teacher can attract other individual in another population to become his own population member. Opposition-based learning is introduced to improve the learning ability of teacher. Comparison with related algorithms is given on some classical benchmark functions. The results show that the proposed algorithm has better convergence rate and accuracy for numerical optimization, suitable to solve the high dimensional optimization problem.

Key words: teaching learning based optimization, premature, double populations, competition, opposition-based learning