| Course Name | Optimization Techniques in Engineering |
| Course Code | 19MEE333 |
| Program | B. Tech. in Mechanical Engineering |
| Year Taught | 2019 |
Introduction – Engineering applications – Statement of an optimization problem – Classifications of Optimization problems – Optimal problem formulation: Problems involving design and manufacturing – Optimality criteria – Classical optimization techniques – Kuhn-Tucker (KT) optimality conditions.
Non-linear programming: One dimensional minimization methods – Unconstrained optimization techniques – Constrained optimization techniques – Transformation methods – Interior and exterior penalty function method – Convergence and divergence of optimization algorithms – Complexity of algorithms.
Modern Methods in Optimization: Genetic Algorithm – Simulated Annealing – Particle Swarm Optimization – Neural Network based optimization – Optimization of Fuzzy systems – Multi-Objective optimization – Data Analytics and optimization using Machine learning approach.
Implementing optimization algorithmsinMatlab / R / Python environment and solving linear, non-linear, multi- objective un constrained and constrained optimization problems.
Course Objectives
Course Outcomes
CO – PO Mapping
| PO/PSO/ CO |
PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | PSO3 |
| CO1 | 2 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | ||||||
| CO2 | 1 | 3 | 2 | 1 | 1 | 1 | |||||||||
| CO3 | 1 | 3 | 2 | 1 | 1 | 2 | 1 | ||||||||
| CO4 | 2 | 2 | 2 | 2 | 2 | 2 | 1 | ||||||||
| CO5 | 1 | 3 | 3 | 2 | 3 | 2 | 2 | 1 |
Textbook(s)
Evaluation Pattern
| Assessment | Internal | External |
| Periodical 1 (P1) | 15 | – |
| Periodical 2 (P2) | 15 | – |
| *Continuous Assessment (CA) | 20 | – |
| End Semester | – | 50 |
| *CA – Can be Quizzes, Assignment, Projects, and Reports. | ||
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