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  Home > Academic/Programmes > Programme Structure > CSE (2017)
Core/Elective: Core Semester: 5 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

Course Content

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

2. Genetic Algorithm-Introduction-Working principle-Representation-selection-fitness assignment-reproduction-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

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

4. Particle Swarm Optimization-Introduction- Working principles- Parameter selection- Neighborhoods and Topologies-Convergence-Artificial Bee Colony Algorithm-Introduction- Working principles- Applications-Cuckoo search based algorithm-Introduction- Working principles- Random walks and the step size-Modified cuckoo search

5. Hybrid Algorithms-Concepts- divide and conquer- decrease and conquer-HPABC-HBABC-HDABC-HGABC-Shuffled Frog Leaping Algorithm-- Working principles -Parameters- Grenade Explosion Algorithm-Working principle-Applications


1. DAN SIMON, Evolutionary Optimization Algorithms, Wiley; 1 edition, 2013
2. XIN-SHE YANG, Engineering Optimization: An Introduction with Metaheuristic Applications, Wiley; 1 edition, 2010
3. S.S. Rao, Engineering Optimization : Theory and Practice, New Age International Pvt. Ltd.; 4th edition , 2013
4. R. Venkata Rao, Teaching Learning Based Optimization Algorithm: And Its Engineering Applications, Springer; 1st ed. 2016

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