Weighted Teaching-Learning-Based Optimization for Global Function Optimization

作     者：Suresh Chandra Satapathy Anima Naik K. Parvathi 

作者机构：Anil Neerukonda Institute of Technology and Sciences Vishakapatnam India Centurion University of Technology and Management Paralakhemundi India Majhighariani Institute of Technology & Science Rayagada India 

出 版 物：《Applied Mathematics》 (应用数学（英文）)

年 卷 期：2013年第4卷第3期

页      面：429-439页

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

主　　题：Function Optimization TLBO Evolutionary Computation 

摘      要：Teaching-Learning-Based Optimization (TLBO) is recently being used as a new, reliable, accurate and robust optimization technique scheme for global optimization over continuous spaces [1]. This paper presents an, improved version of TLBO algorithm, called the Weighted Teaching-Learning-Based Optimization (WTLBO). This algorithm uses a parameter in TLBO algorithm to increase convergence rate. Performance comparisons of the proposed method are provided against the original TLBO and some other very popular and powerful evolutionary algorithms. The weighted TLBO (WTLBO) algorithm on several benchmark optimization problems shows a marked improvement in performance over the traditional TLBO and other algorithms as well.