Design and Optimization of Energy Systems

IIT Madras Course , Prof. C. Balaji

Lecture 38: Mod-01 Lec-38 Genetic Algorithms

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Lecture Details :

Design and Optimization of Energy Systems by Prof. C. Balaji , Department of Mechanical Engineering, IIT Madras. For more details on NPTEL visit http://nptel.iitm.ac.in

Course Description :

Module 1: Introduction
Introduction to design and specifically system design.
Morphology of design with a flow chart.
Very brief discussion on market analysis, profit, time value of money, an example of discounted cash flow technique.
Concept of workable design, practical example on workable system and optimal design.
Module 2 : System Simulation
Classification.
Successive substitution method - examples.
Newton Raphson method - one unknown - examples.
Newton Raphson method - multiple unknowns - examples.
Gauss Seidel method - examples.
Rudiments of finite difference method for partial differential equations, with an example.
Module 3: Regression and Curve Fitting
Need for regression in simulation and optimization.
Concept of best fit and exact fit.
Exact fit - Lagrange interpolation, Newton's divided difference - examples.
Least square regression - theory, examples from linear regression with one and more unknowns - examples.
Power law forms - examples.
Gauss Newton method for non-linear least squares regression - examples.
Module 4: Optimization
Introduction.
Formulation of optimization problems examples.
Calculus techniques Lagrange multiplier method proof, examples.
Search methods Concept of interval of uncertainty, reduction ratio, reduction ratios of simple search techniques like exhaustive search, dichotomous search, Fibonacci search and Golden section search numerical examples.
Method of steepest ascent/ steepest descent, conjugate gradient method examples.
Geometric programming examples.
Dynamic programming examples.
Linear programming two variable problem graphical solution.
New generation optimization techniques Genetic algorithm and simulated annealing - examples.
Introduction to Bayesian framework for optimization- examples.

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