{{{credits}}}
| L | T | P | C |
| 3 | 0 | 0 | 3 |
- To Learn bio-inspired theorem and algorithms.
- To understand foundations of complex systems and theoretical biology.
- To Understand random walk and simulated annealing.
- To Learn genetic algorithm and differential evolution .
- To Learn swarm optimization and ant colony for feature selection.
- To understand application of various algorithms.
{{{unit}}}
| Unit I | Introduction | 9 |
Introduction to Algorithms: Newton’s method – Optimization – No-Free-Lunch theorems – Nature-Inspired metaheuristics; Analysis of Algorithms: Nature inspires algorithms – Parameter tuning and parameter control
{{{unit}}}
| Unit II | Random Walks and Annealing | 9 |
Random Walks: Random variables – Isotropic random walks – Levy distribution and flights – Markov chains – Step sizes and search efficiency – Modality and intermittent search strategy – Importance of randomization – Eagle strategy; Annealing: Annealing and Boltzmann distribution – Parameters – SA algorithm – Stochastic tunnelling
{{{unit}}}
| Unit III | Genetic Algorithms and Differential Evolution | 9 |
Introduction to genetic algorithms – Role of genetic operators – Choice of parameters – GA variants – Schema theorem – Convergence analysis; Introduction to differential evolution – Variants – Choice of parameters – Convergence analysis – Implementation
{{{unit}}}
| Unit IV | Swarm Optimization and Firefly Algorithm | 9 |
Swarm Intelligence – PSO algorithm – Accelerated PSO – Implementation – Convergence analysis – Binary PSO; The Firefly algorithms – Algorithm analysis – Implementation – Variants; Cuckoo behaviour – Cuckoo search
{{{unit}}}
| Unit V | Bat Flower Pollination Ant and Bee Algorithms | 9 |
Echolocation of bats – Bat algorithms – Binary Bat algorithms – Variants of the Bat algorithm; Flower pollination algorithms – Multi-Objective Flower pollination algorithms – validation and Numerical experiments;Ant algorithms – Bee-inspired algorithms
\hfill Total: 45
After the completion of this course, students will be able to:
- Understand principles of biologically inspired computing (K2)
- Understand random walk and simulated annealing (K2)
- Apply genetic algorithms (K3)
- Solve problems using swarm intelligence and cuckoo search (K3)
- Apply bio-inspired algorithms to solve real world problems (K3)
- Xin-She Yang, “Nature Ispired Optimization Algorithm”, Elsevier First Edition, 2014
- Eiben A.E., Smith, James E, “Introduction to Evolutionary Computing”, Springer, 2015
- Stephan Olariu, Albert Y. Zomaya, “Handbook of Bioinspired Algorithms and Application”, Chapman & Hall/CRC, 2006
- Dan Simon, “ Evolutionary Optimization Algorithms”. John Wiley & Sons, 2013
- Helio J.C. Barbosa, “Ant Colony Optimization - Techniques and Applications”, Intech, 2013
- Yang, Cui,XIao,Gandomi,Karamanoglu ,“Swarm Intelligence and Bio-Inspired Computing”, Elsevier First Edition, 2013