Teaching–Learning-Based Optimization: An optimization method for continuous non-linear large scale problems

2012 ◽  
Vol 183 (1) ◽  
pp. 1-15 ◽  
Author(s):  
R.V. Rao ◽  
V.J. Savsani ◽  
D.P. Vakharia
Keyword(s):  
Large Scale ◽  
Optimization Method ◽  
Large Scale Problems ◽  
Non Linear ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

A New Type of Optimization Method Based on Conjugate Directions

2013 ◽  
Vol 347-350 ◽  
pp. 3029-3034
Author(s):  
Xin Pan
Keyword(s):  
Large Scale ◽  
Line Search ◽  
Optimization Method ◽  
New Method ◽  
Conjugate Directions ◽  
Exact Line Search ◽  
Large Scale Problems ◽  
New Type ◽  
Quadratic Termination

A new type of optimization method based on conjugate directions is proposed in this paper. It can be proved that this type of method has quadratic termination property without exact line search. The new method requires only the storage of 4 vectors such that it is suitable for large scale problems. Numerical experiences show that the new method is effective.

Synthesis of thinned planar antenna arrays using teaching–learning-based optimization

2014 ◽  
Vol 7 (5) ◽  
pp. 557-563 ◽  
Author(s):  
Nihad I. Dib
Keyword(s):  
Antenna Arrays ◽  
Optimization Problems ◽  
Optimization Method ◽  
Side Lobe ◽  
Side Lobe Level ◽  
Planar Antenna ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning ◽  
Level Reduction ◽  
Better Than

In this paper, the design of thinned planar antenna arrays of isotropic radiators with optimum side lobe level reduction is studied. The teaching–learning-based optimization (TLBO) method, a newly proposed global evolutionary optimization method, is used to determine an optimum set of turned-ON elements of thinned planar antenna arrays that provides a radiation pattern with optimum side lobe level reduction. The TLBO represents a new algorithm for optimization problems in antenna arrays design. It is shown that the TLBO provides results that are better than (or the same as) those obtained using other evolutionary algorithms.

scholarly journals Advanced Teaching-Learning-Based Optimization Algorithm for Actual Power Loss Reduction

2019 ◽  
Vol 9 (1) ◽  
pp. 46
Author(s):  
Lenin Kanagasabai
Keyword(s):  
Optimization Algorithm ◽  
Power Loss ◽  
Reactive Power ◽  
Learning Procedure ◽  
Power Loss Reduction ◽  
Non Linear ◽  
Teaching Learning Based Optimization ◽  
Weighted Factor ◽  
Teaching Learning ◽  
Advanced Teaching

In this work Advanced Teaching-Learning-Based Optimization algorithm (ATLBO) is proposed to solve the optimal reactive power problem. Teaching-Learning-Based Optimization (TLBO) optimization algorithm has been framed on teaching learning methodology happening in classroom. Algorithm consists of “Teacher Phase”, “Learner Phase”. In the proposed Advanced Teaching-Learning-Based Optimization algorithm non-linear inertia weighted factor is introduced into the fundamental TLBO algorithm to manage the memory rate of learners.  In order to control the learner’s mutation arbitrarily during the learning procedure a non-linear mutation factor has been applied. Proposed Advanced Teaching-Learning-Based Optimization algorithm (ATLBO) has been tested in standard IEEE 14, 30 bus test systems and simulation results show the proposed algorithm reduced the real power loss effectively.

scholarly journals A Cyclical Non-Linear Inertia-Weighted Teaching–Learning-Based Optimization Algorithm

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 94 ◽  
Author(s):  
Zongsheng Wu ◽  
Ru Xue
Keyword(s):  
Global Optimization ◽  
Local Minimum ◽  
Optimization Problems ◽  
Tlbo Algorithm ◽  
Comparison Results ◽  
Non Linear ◽  
Significant Performance ◽  
Teaching Learning Based Optimization ◽  
Weighted Factor ◽  
Teaching Learning

After the teaching–learning-based optimization (TLBO) algorithm was proposed, many improved algorithms have been presented in recent years, which simulate the teaching–learning phenomenon of a classroom to effectively solve global optimization problems. In this paper, a cyclical non-linear inertia-weighted teaching–learning-based optimization (CNIWTLBO) algorithm is presented. This algorithm introduces a cyclical non-linear inertia weighted factor into the basic TLBO to control the memory rate of learners, and uses a non-linear mutation factor to control the learner’s mutation randomly during the learning process. In order to prove the significant performance of the proposed algorithm, it is tested on some classical benchmark functions and the comparison results are provided against the basic TLBO, some variants of TLBO and some other well-known optimization algorithms. The experimental results show that the proposed algorithm has better global search ability and higher search accuracy than the basic TLBO, some variants of TLBO and some other algorithms as well, and can escape from the local minimum easily, while keeping a faster convergence rate.

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