Modified Teaching-Learning-Based Optimization Algorithm by Using Simulated Annealing

摘要:

针对教与学优化算法(TLBO)在解决复杂优化问题时易陷入局部最优的缺点,提出了一种融合模拟退火的改进教与学优化算法(SAMTLBO).该算法首先对学员阶段做了改进,在保持TLBO算法简单易实现的基础上,利用模拟退火方法增强了TLBO算法摆脱局部最优的能力,最后用4种算法对8个无约束优化函数仿真.数值实验表明,该算法无论是在收敛速度还是在寻优精度上均优于基本TLBO算法、ETLBO算法和DMTLBO算法.

As for the disadvantage in local optima of Teaching-Learning-Based Optimization algorithm （TLBO） in solving complex optimization problem, a modified Teaching-Learning-Based Optimization by using simulated annealing （SAMTLBO） is proposed. The algorithm firstly makes an improvement in students stage. On the basic of keep TLBO easily implement and we utilize simulated annealing method to enhance TLBO algorithm to get rid of the ability of its local optimum. Finally we apply four kinds of algorithms to simulate nu-constrained optimization functions. Numerical experiments show that SAMTLBO algo- rithm is better than basic TLBO algorithm, ETLBO algorithm and DMTLBO algorithm in terms of convergence speed and search precision.

作者:

岳振芳 高岳林

机构地区:

宁夏大学数学计算机学院 北方民族大学信息与系统科学研究所

出处:

《betway官方app 学报：自然科学版》 CAS 北大核心 2016年第1期149-154,共6页

基金:

国家自然科学基金(61561001) 北方民族大学重点科研项目(2015KJ10)

关键词:

教与学优化算法 模拟退火算法 局部最优

teaching-learning-based optimization algorithm simulated annealing algorithm local optima

分类号:

TP18 [自动化与计算机技术—控制理论与控制工程]

融合模拟退火的改进教与学优化算法.pdf