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Presentation
Introduction
Problem formulation
Region elimination method
Multivariable problem
Convex function
Multivariable problem with constraints
Quadratic approximation
Transformation method
Linear Problem
Genetic Algorithms
Particle swarm optimization
Differential Evolution

 

Matlab code
Bounding phase
Exhaustive search
Golden section method
Interval halving method
DrawContourSurface
Simple PSO
Simple 1+1 ES
Simple Differential Evolution

 

Video
Univariate method
Steepest descent method
Newton's method
Exterior penalty method
Interior penalty method
  CE 602: Optimization Method

Instructor: Rajib Kumar Bhattacharjya

Room No. 105 (M Block)

Phone No. 2428

Email: rkbc@iitg.ernet.in

 

 

Class Timing

 

Tuesday         : 16.00 - 16.55 (Room No. 3102)

Wednesday    : 15.00 - 15.55 (Room No. 3102)

Thursday       : 18.00 - 18.55 (Room No. 3102)

 

Pre-requisites: Nil

 

Syllabus

 

Basics of engineering analysis and design, need for optimal design, formulation of optimal design problems, basic difficulties associated with solution of optimal problems. Classical optimization methods, necessary and sufficient optimality criteria for unconstrained and constrained problems, Kuhn-Tucker conditions, global optimality and convex analysis. Linear optimal problems, Simplex method, Introduction to Karmarkar's algorithm; numerical methods for nonlinear unconstrained and constrained problems, sensitivity analysis, linear post optimal analysis. Sensitivity analysis of discrete and distributed systems; introduction to variational methods of sensitivity analysis, shape sensitivity. Introduction to integer programming, dynamic programming, stochastic programming and geometric programming. Introduction to genetic algorithm and simulated annealing.

 

Texts:

  1. Deb K. Optimization for Engineering Design: Algorithms and Examples. PHI Pvt Ltd., 1998.

  2. Belegundu A.D. and Chandrupatla T.R. Optimization Concepts and Applications in Engineering. Pearson Education Asia 2002.

  3. Rao S.S. Engineering Optimization Theory and Practice. New Age International (P) Ltd. 2001.

  4. J. S. Arora, Introduction to Optimum Design, McGraw Hill International Edition, 1989.

 

References:

  1. Goldberg D.E. Genetic Algorithms in Search, Optimization and Machine Learning. Pearson Education Asia 2002

  2. Hafta R. T. and Gurdal Z. Elements of Structural Optimization. Third Revised and Expanded Edition. Kluwer Academic Publishers 1996.

  3. Deb K. Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, UK, 2001.

  4. K. Srinivasa Raju and D. Nagesh Kumar. Multicriterion Analysis in Engineering and Management. PHI Learning Pvt. Ltd., New Delhi, India 2010.

 

 

 

Method of Assessment

 

Type

Marks

Assignments

10

Quiz

15

Mid-semester Exam

25

End-semester Exam

40

Mini group project

10

Total

100

 

 

 

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