Unit 1 Introduction to Optimization

4 lecture hours

• Historical development
• Engineering Applications of Optimization
• Statement of an Optimization Problem
• A brief overview on classification of optimization problems [1]

Unit 2 Single Variable Optimization Algorithms

10 lecture hours

• Optimality criteria
• Bracketing methods – Exhaustive Search Method
• Bounding Phase Method
• Region Elimination methods – Interval Halving Method
• Fibonacci Search Method
• Golden Section Search Method
• Point Estimation method – Successive Quadratic Estimation Method
• Gradient based methods -Newton Raphson Method
• Bisection Method
• Secant Method
• Cubic Search Method [2]

Unit 3 Multivariable Optimization Algorithms

• Optimality criteria
• Unidirectional search
• Direct Search methods – Box’s Evolutionary Optimization Method
• Simplex Search Method
• Hooke Jeeves Pattern Search Method
• Gradient based methods - Cauchy’s Method
• Newton’s Method
• Marquardt’s Method
• Conjugate Gradient Method [2] 09 Program Name: Engineering Level: PG Branch: Electrical Engineering Course / Subject Code: ME01000171 Course / Subject Name: Optimization Techniques

Unit 4 Constrained Optimization Algorithms

11 lecture hours

• Kuhn Tucker Conditions
• Lagrangian Duality Theory
• Transformation Methods - Penalty function Method
• Method of Multipliers
• Direct Search for Constrained Minimization – Variable Elimination Method
• Complex Search Method
• Random Search Methods
• Linearized Search Methods – Frank Wolfe Method
• Cutting Plane Method [2]

Unit 5 Modern Methods of Optimization- Introduction, Genetic Algorithms,

11 lecture hours

• Simulated Annealing
• Particle Swarm Optimization
• Ant Colony Optimization
• Optimization of Fuzzy Systems
• Neural Network based optimization
• Teaching Learning Based Optimization