scholarly journals High dimensional real parameter optimization with teaching learning based optimization

2012 ◽  
Vol 3 (5) ◽  
pp. 807-816 ◽  
Author(s):  
Suresh Chandra Satapathy ◽  
Anima Naik ◽  
K Parvathi
Keyword(s):  
Parameter Optimization ◽  
Real Parameter ◽  
High Dimensional ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Teaching-Learning-Based Optimization Algorithm for Dealing with Real-Parameter Optimization Problems

2013 ◽  
Vol 380-384 ◽  
pp. 1342-1345 ◽  
Author(s):  
Kai Lin Wang ◽  
Hui Bin Wang ◽  
Li Xia Yu ◽  
Xue Yu Ma ◽  
Yun Sheng Xue
Keyword(s):  
Parameter Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Real Parameter ◽  
Experimental Comparison ◽  
Practical Applications ◽  
Evaluation Functions ◽  
Tlbo Algorithm ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

A latest optimization algorithm, named Teaching-Learning-Based Optimization (simply TLBO) was proposed by R. V. Rao et al, at 2011. Afterwards, some improvements and practical applications have been conducted toward TLBO algorithm. However, as far as our knowledge, there are no such works which categorize the current works concerning TLBO from the algebraic and analytic points of view. Hence, in this paper we firstly introduce the concepts and algorithms of TLBO, then survey the running mechanism of TLBO for dealing with the real-parameter optimization problems, and finally group its real-world applications with a categorizing framework based on the clustering, multi-objective optimization, parameter optimization, and structure optimization. The main advantage of this work is to help the users employ TLBO without knowing details of this algorithm. Meanwhile, we also give an experimental comparison for demonstrating the effectiveness of TLBO on 5 benchmark evaluation functions and conclude this work by identifying trends and challenges of TLBO research and development.

Multiobjective optimization of in situ process parameters in preparation of Al-4.5%Cu–TiC MMC using a grey relation based teaching–learning-based optimization algorithm

2017 ◽  
Vol 232 (4) ◽  
pp. 393-407 ◽  
Author(s):  
Biswajit Das ◽  
Susmita Roy ◽  
RN Rai ◽  
SC Saha
Keyword(s):  
Objective Function ◽  
Metal Matrix Composite ◽  
Parameter Optimization ◽  
Process Parameters ◽  
Optimization Algorithm ◽  
Metal Matrix ◽  
Optimization Problems ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

In modern in situ composite fabrication processes, the selection of optimal process parameters is greatly important for the preparation of best quality metal matrix composite. For achieving high-quality composite, an efficient optimization technique is essential. The present study explores the potential of a new robust algorithm named teaching–learning-based optimization algorithm for in situ process parameter optimization problems in fabrication of Al-4.5%Cu–TiC metal matrix composite fabricated by stir casting technique. Optimization process is carried out for optimizing the in situ processing parameters i.e. pouring temperature, stirring speed, reaction time for achieving better mechanical properties, i.e. better microhardness, toughness, and ultimate tensile strength. Taguchi’s L25 orthogonal array design of experiment was used for performing the experiments. Grey relational analysis is used for the conversion of the multiobjective function into a single objective function, which is being used as the objective function in the teaching–learning-based optimization algorithm. Confirmation test results show that the developed teaching–learning-based optimization model is a very efficient and robust approach for engineering materials process parameter optimization problems.

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