PX4014 Optimization Techniques Syllabus:

PX4014 Optimization Techniques Syllabus – Anna University PG Syllabus Regulation 2021

OBJECTIVES:

Students will be able to:
 understand the classification of optimization
 study the linear programming models and solution techniques
 study the different non-linear programming problem solution techniques
 understand the concept of dynamic programming
 study the fundamentals genetic algorithm and it applications.

UNIT I INTRODUCTION

Definition, Classification of optimization problems, Classical Optimization Techniques, Single and Multiple Optimization with and without inequality constraints.

UNIT II LINEAR PROGRAMMING (LP)

Simplex method of solving LPP, revised simplex method, duality, Constrained optimization, Theorems and procedure, Linear programming, mathematical model, solution technique, duality.

UNIT III NON LINEAR PROGRAMMING

Steepest descent method, conjugates gradient method, Newton’s Method, Sequential quadratic programming, Penalty function method, augmented Lagrange multiplier method.

UNIT IV DYNAMIC PROGRAMMING (DP)

Multistage decision processes, concept of sub-optimization and principle of optimality, Recursive relations, Integer Linear programming, Branch and bound algorithm

UNIT V GENETIC ALGORITHM

Introduction to genetic Algorithm, working principle, coding of variables, fitness function, GA operators; Similarities and differences between Gas and traditional methods; Unconstrained and constrained optimization using genetic Algorithm, real coded gas, Advanced Gas, global optimization using GA, Applications to power system.

TOTAL : 45 PERIODS

OUTCOMES:

Students will be able to:
CO1:learn about different classifications of optimization problems and techniques.
CO2:attain knowledge on linear programming concepts
CO3:understand the application of non-linear programming in optimization techniques
CO4:understand the fundamental concepts of dynamic programming
CO5:gain knowledge about Genetic algorithm and its application to power system optimization.

REFERENCES:

1. S.S. Rao, “Engineering Optimization – Theory and Practice”, John Wiley & Sons, Inc.,2009.
2. Hamdy A. Taha, Operations Research: An Introduction, 10th Edition, Pearson, 2016.
3. David G. Luenberger, “Introduction to Linear and Nonlinear Programming”, Addison- Wesley, 1973.
4. E. Polak, “Computational methods in Optimization”, Academic Press,1971.
5. Pierre D.A., “Optimization Theory with Applications”, Wiley Publications,1969.