scholarly journals Multi-pass turning process parameter optimization using teaching–learning-based optimization algorithm

2013 ◽  
Author(s):  
R. Venkata Rao ◽  
V.D. Kalyankar
Keyword(s):  
Parameter Optimization ◽  
Optimization Algorithm ◽  
Process Parameter ◽  
Turning Process ◽  
Process Parameter Optimization ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

Parallel Turning Process Parameter Optimization Based on a Novel Heuristic Approach

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Lie Tang ◽  
Robert G. Landers ◽  
S. N. Balakrishnan
Keyword(s):  
Parameter Optimization ◽  
Search Algorithm ◽  
Process Parameter ◽  
Computational Time ◽  
Turning Process ◽  
Machining Time ◽  
Process Parameter Optimization ◽  
Turning Operations ◽  
Operation Optimization ◽  
Machining Operations

Process parameter optimization has been widely investigated in single-tool machining operations. However, for multitool machining operation optimization, the research reported in literature is scarce. In this paper, a novel heuristic algorithm based on particle swarm optimization (PSO) is proposed to optimize, in terms of minimum machining time, the process parameters for two-tool parallel turning operations with n features. Both single-pass and multipass operations are considered. The simulation results show that the performance of the proposed algorithm, in terms of total machining time and required computational time, is superior to an exhaustive search algorithm.

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.

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.

Improved Teaching-Learning-Based Optimization Algorithm and its Application in PID Parameter Optimization

2019 ◽  
Vol 13 (2) ◽  
pp. 1-17
Author(s):  
Fahui Gu ◽  
Wenxiang Wang ◽  
Luyan Lai
Keyword(s):  
Parameter Optimization ◽  
Optimization Algorithm ◽  
Adaptive Learning ◽  
Teaching And Learning ◽  
Optimization Problems ◽  
Learning Algorithm ◽  
Global Search ◽  
Opposition Based Learning ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

The teaching-learning-based optimization (TLBO) algorithm has been applied to many optimization problems, but its theoretical basis is relatively weak, its control parameters are difficult to choose, and it converges slowly in the late period and makes it too early to mature. To overcome these shortcomings, this article proposes a dual-population co-evolution teaching and learning optimization algorithm (DPCETLBO) in which adaptive learning factors and a multi-parent non-convex hybrid elite strategy are introduced for a population with high fitness values to improve the convergence speed of the algorithm, while an opposition-based learning algorithm with polarization is introduced for a population with lower fitness values to improve the global search ability of the algorithm. In a proportion integration differentiation (PID) parameter optimization experiment, the simulation results indicate that the convergence of the DPCETLBO algorithm is fast and precise, and its global search ability is superior to those of the TLBO, ETLBO and PSO algorithms.

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