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