Course

Unit 1

Introduction to Optimization – Engineering applications – Statement of an optimization problem – Classification – Optimal problem formulation: Problems in design and manufacturing fields – Optimality criteria – Classical optimization techniques – Kuhn-Tucker (KT) optimality conditions.

Unit 2

Non-linear programming algorithms: One-dimensional problem, Unconstrained optimization problem, Constrained optimization problem – Transformation methods – Interior and exterior penalty function method – Convergence and divergence of optimization algorithms – Complexity of algorithms.

Unit 3

Modern Methods in Optimization: Genetic Algorithm – Simulated Annealing – Particle Swarm Optimization – Neural Network-based optimization – Optimization of Fuzzy systems – Multi-Objective optimization – Optimization in the probabilistic domain – Shape and Topology optimization – Data Analytics and optimization using Machine learning approach.

Course Objectives

• Introduce the traditional and modern methods of optimization techniques used for solving non-linear unconstraint and constraint engineering optimization problems.
• Considering the computational aspects, the course will involve a significant number of computational assignments using software tools and a term project in the area of engineering optimization.

Course Outcomes

Skills Acquired

Formulate the engineering problems as an optimization problem; Select appropriate solution methods and strategies and solve optimization problems; Solving complex optimization problems using heuristic/ meta heuristic approach; Solve Engineering Design and Manufacturing related optimization problems using software tools.

1. Rao, Singiresu “Engineering optimization: theory and practice”. John Wiley & Sons, 2019.
2. Deb, “Optimization for engineering design: Algorithms and examples”. PHI Learning Pvt. Ltd., 2012.
3. Arora, S., “Introduction to Optimum Design”, Academic Press, 4^th Edition, 2017.