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19-475-0304: ADVANCED OPTIMIZATION TECHNIQUES
Core/Elective: Elective Semester: 3 Credits: 4
Course Description

This course is about the well-known population-based optimization techniques developed during last three decades. This course emphasizing on the advanced optimization techniques to solve large-scale problems especially with nonlinear objective functions

Course Objectives

To study concepts of Population based Optimization techniques
To understand the mathematical foundations for various advanced optimization techniques
To apply the algorithms to various inter disciplinary applications

Course Content

Module I
Introduction to optimization- formulation of optimization problems-Review of classical methods-Linear programming-Nonlinear programming-Constraint optimality criteria-constrained optimization-Population based optimization techniques

Module II
Genetic Algorithm-Introduction-Working principle-Representation-selection-fitness assignmentreproduction-cross over-mutation-constraint handling-advanced genetic algorithms-Applications- Artificial Immune Algorithm-Introduction-Clonal selection algorithm- Negative selection algorithm-Immune network algorithms-Dendritic cell algorithms

Module III
Differential Evolution-Introduction-Working principles-parameter selection-advanced algorithms in Differential evolution-Biogeography-Based Optimization-Introduction-Working Principles- Algorithmic variations

Module IV
Particle Swarm Optimization-Introduction- Working principles- Parameter selection- Neighborhoods and Topologies-Convergence-Artificial Bee Colony Algorithm-Introduction- Working principles- ApplicationsCuckoo search based algorithm-Introduction- Working principles- Random walks and the step size-Modified cuckoo search

Module V
Hybrid Algorithms-Concepts- divide and conquer- decrease and conquer-HPABC-HBABC-HDABCHGABC-Shuffled Frog Leaping Algorithm-- Working principles -Parameters- Grenade Explosion Algorithm-Working principle-Applications

REFERNCES

1. Dan Simon, Evolutionary Optimization Algorithms, 1e, Wiley, 2013
2. Xin-She Yang, Engineering Optimization: An Introduction with Meta-heuristic Applications, 1e, Wiley, 2010
3.S.S. Rao, Engineering Optimization: Theory and Practice, 4e,New Age International, 2013
4.R. VenkataRao, Teaching Learning Based Optimization Algorithm: And Its Engineering Applications, 1e, Springer, 2016


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