Credits : 3

Prerequisites : -

Compulsory/Optional : Optional

Aim(s)
To provide important optimization algorithms including single variable optimization algorithm, multivariable optimization algorithm, constrained optimization algorithm, and non-conventional optimization algorithms.
Intended Learning Outcomes
On successful completion of the course, the students should be able to; Make comparisons, analyze and synthesize various engineering and business applications pertaining to optimization via modeling. Classify and identify the most suitable method of solving an optimization problem. Solve standard optimization problems manually. Use software to solve optimization problem with any degree of complexity. Apply optimization techniques for advance research.
Course content/Course description
Single variable optimization algorithms: Bracketing methods, Region elimination methods, Gradient based methods Multivariable optimization algorithms: Direct search methods, Gradient based methods Constrained optimization algorithms: Transformation methods, Direct search, Linearized search Non- conventional optimization algorithms: Genetic algorithms, Simulated annealing, Global optimization