Kind Attn. Engg. students, don't turn a blind eye to this one, it may do wonders to you.It is a unique NATURE INSPIRED technique free from Algo Specific Parameters, unlike others , gives accurate results and is the easiest method of optimisation known to me so far.

1. Teaching - Learning Based
Optimization technique
- A brief insight to an upcoming technique for optimization.
Compiled & Presented By:
Smriti M.
B.Tech. (Production S/W)
111013081

2. Introduction to Optimization
techniquesEngineering design and manufacturing can be characterized as,
Constrained
Decision making
process
that satisfy well – defined human needs
Goal oriented &

3. Introduction to Optimization
techniques• Goals
• Constraints
( Search Space)
Reduction in wear,
corrosion, surface
roughness, production
time increase in tool
life etc.
Available Force,
power, speed, feed etc.
Answer to finding the most
suitable solution -
OPTIMIZATION TECHNIQUES

4. Classification of
Optimization techniques
Conventional Non-Conventional
WE ARE HERE

5. Need for Non-Conventional
methods.
Shortcomings of Conventional method
Inaccurate results when the number of variables increases.
Cannot handle non – linear character.

6. Nature inspired Heuristic Optimization
methods
1. Genetic Algorithm
2. Particle Swarm Optimization
3. Artificial Bee Colony
4. Ant Colony Optimization
5. Harmony Search
and many more…………….
Need for Non-Conventional
methods.

7. Need for Teaching –
Learning Based
Optimization techniqueDifficult to determine optimum algorithm controlling
parameters.
Improper tuning leads to
• Chances of getting a local optimal solution
• Increase in computational effort

8. Only common controlling parameters like
• Population size
• Number of Generations
Need for Teaching –
Learning Based
Optimization technique
Does not require Algorithm Specific Controlling Parameters.
To obtain global solutions for continuous non-linear
functions with less computational effort and high
consistency.

9. Introduction to TLBO
• Teaching-learning is an important process where every
individual tries to learn something from other individuals to
improve themselves .
• It simulates the traditional teaching learning phenomenon of a
classroom. The algorithm simulates two fundamental modes of
learning:
1. Through the teacher (teacher phase) and,
2. Interacting with other learners (learner phase).
• The influence of a teacher on the output of learners in a class.
The algorithm mimics the teaching–learning ability of teacher
and learners in a classroom.

10. TLBO  A population – based Algorithm.
Introduction to TLBO
Analogies
Group of students
Different Subjects
Result Scores
Teacher
Population
Different Design Variables
Fitness Value of the
problem

11. Pre-requisites:
 Formulate the Obj. Func.
Maximization OR Minimization.
( Could be Single OR Multi-Objective Func. )
Eg. τ = τs + (V / M) (1 + (τtc / T )) + τ0
 Constraints must be specified.
Eg. amin < a < amax ;
fmin < f < fmax ;
vmin < v < vmax
Methodology of TLBO
Fc = CF * vα * fβ * aγ
R = (125* f2 ) / rE ≤ RMAX,

12. Distribution of Marks of
Students in a class
Marks Amongst students of a class is
assumed to follow a
Normal Distribution.

13. Teacher Phase
m  number of subjects
n  number of learners
Mj,i  mean result of the class where, j= 1,2,3
Best Learner  “TEACHER” for that sequence

14. Teacher Phase
Let Xtotal – kbest , i be the result of the best learner, then

15. Learner Phase
Let P and Q be two learners such that,
Updated values of the
Learner’s Phase
Updated
values of the
Teacher’s
Phase

16. Case Study – Validation of
optimised factors of MQL
using TLBO
Input parameters : Cutting speed, feed, depth of cut.
Output parameters : Surface Roughness.

17. 1st order regression
equation

18. 2nd order regression
equationResults obtained using G.A

19. Validation using TLBO.
1. Generation of initial population. RANDOM

20. Validation using TLBO
…contd.
2. Teacher’s Phase
X teacher
a.k.a
X kbest
a.k.a
M new
Mean =
231.551
Mean =
0.05
Mean =
0.1003

22. 3. Learner Phase
Let P and Q be two learners such that,
Leaner P
Leaner Q

23. Results obtained by G.A
Results obtained by TLBO
Results.

24. Future Prospects.
1. Increase in the number of teachers.
2. Adaptive teaching factor.
3. Learning tutorial.
4. Self-motivated learning
5. Method of Elitism

25. Conclusion
1. Does not require Algorithm Specific Controlling
Parameters
2. Easy & Effective
3. Lower Number of Iterations
4. It can be applied to practically any part of Life
[says Rao]

26. References
[1] R. Venkata Rao and Vivek Patel ; Department of Mechanical Engineering, S.V. National
Institute of Technology, Ichchanath, Surat, Gujarat – 395 007, India ; “An elitist teaching-
learning-based optimization algorithm for solving complex constrained optimization
problems” ; International Journal of Industrial Engineering Computations ; 2012 Growing
Science Ltd.
[2] R. Venkata Rao, Vivek Patel ; “An improved teaching-learning-based optimization
algorithm for solving unconstrained optimization problems” ; 1026-3098 © 2013 Sharif
University of Technology. Production and hosting by Elsevier B.V.
[3] R. Venkata Rao, V.D. Kalyankar ; “Multi-pass turning process parameter optimization
using teaching–learning-based optimization algorithm”; 1026-3098 © 2013 Sharif University
of Technology. Production and hosting by Elsevier B.V.
[4] R.V. Rao , V.J. Savsani, D.P. Vakharia; “Teaching–Learning-Based Optimization: An
optimization method for continuous non-linear large scale problems” ; © 2011 Elsevier Inc.

27. References
[5] R.R Deshmukh; PHD Thesis, 2013, Dr. Babasaheb Ambedkar Marathwada University,
Aurangabad; “Minimum Quantity Lubrication Turning by using G.A and verification by
TLBO”.
[6] Doriana M. D’Addona, Roberto Teti; Department of Chemical, Materials and Production
Engineering, University of Naples Federico II, Piazzale Tecchio 80, Naples 80125, Italy;
“Genetic algorithm-based optimization of cutting parameters in turning processes” ; Forty
Sixth CIRP Conference on Manufacturing Systems 2013.