scholarly journals Comparative performance of an elitist teaching-learning-based optimization algorithm for solving unconstrained optimization problems

2013 ◽  
Vol 4 (1) ◽  
pp. 29-50 ◽  
Author(s):  
R. Venkata Rao ◽  
Vivek Patel
Keyword(s):  
Unconstrained Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Comparative Performance ◽  
Unconstrained Optimization Problems ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

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.

Solving chiller loading optimization problems using an improved teaching-learning-based optimization algorithm

2017 ◽  
Vol 39 (1) ◽  
pp. 65-77 ◽  
Author(s):  
Pei-yong Duan ◽  
Jun-qing Li ◽  
Yong Wang ◽  
Hong-yan Sang ◽  
Bao-xian Jia
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning
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