Course

Course Outcome

Course Articulation Matrix: Correlation level [ 1: low, 2: medium, 3: High]

Prerequisite: Numerical computation and optimization

Definition-Classification of optimization problems-Unconstrained and Constrained optimization, Optimality conditions – Linear and non-linear programming, Quadratic programming, Intelligent Search methods – Evolutionary approaches.?

Fundamentals of Evolutionary algorithms- Simulated annealing (SA) algorithm – Genetic Algorithm (GA) -Genetic Operators – Selection, Crossover and Mutation-Issues in GA implementation – GA based solution for Economic load Dispatch and unit commitment.?

Particle Swarm Optimization (PSO) – principle – parameter selection – Issues in PSO implementation, Differential Evolution (DE) algorithm, applications to Economic Load Dispatch and Optimal power flow.

Tabu search algorithm, Ant Colony Optimization (ACO) – applications to unit commitment.

Introduction to Hybrid optimization methods. Multi Objective Optimization – Concept of pareto optimality – Conventional approaches – Non-dominated sorting/ranking approaches for MOOP – applications to Economic Emission dispatch.?? Simulation case studies.

1. Kalyanmoy Deb, Optimization for Engineering Design, Prentice Hall of India, 2nd edition,2012.
2. D.P.Kothari and J.S.Dhillon, Power System Optimization, 2ndEdition, PHI learning private limited, 2010.
3. Soliman Abdel Hady, Abdelaal Hassan Mantawy, Modern optimization techniques with applications in Electric Power Systems, Springer,2012.?
4. Kalyanmoy Deb, Multi objective optimization using Evolutionary Algorithms, John Wiley and Sons, 2008.?
5. Carlos A.Coello, Gary B. Lamont, David A.Van Veldhuizen, Evolutionary Algorithms for solving Multi Objective Problems, 2nd Edition, Springer, 2008.
6. KwangY.Lee, Mohammed A. El Sharkawi, Modern heuristic optimization techniques, John Wiley and Sons,1st edition, 2008.