AD3001 Bio Inspired Optimization Techniques Syllabus:
AD3001 Bio Inspired Optimization Techniques Syllabus – Anna University Regulation 2021
COURSE OBJECTIVES:
• To understand fundamental topics in bio-inspired optimization techniques
• To Learn the collective systems such as ACO, PSO, and BCO
• To develop skills in biologically inspired algorithm design with an emphasis on solving real world problems
• To understand the most appropriate types of algorithms for different data analysis problems and to introduce some of the most appropriate implementation strategies.
• To implement the Bio-inspired technique with other traditional algorithms.
UNIT I INTRODUCTION
Optimization Techniques: Introduction to Optimization Problems – Single and Muti- objective Optimization – Classical Techniques – Overview of various Optimization methods – Evolutionary Computing: Genetic Algorithm and Genetic Programming: Basic concept – encoding – representation – fitness function – Reproduction – differences between GA and Traditional optimization methods – Applications – Bio- inspired Computing (BIC): Motivation – Overview of BIC – usage of BIC – merits and demerits of BIC.
UNIT II SWARM INTELLIGENCE
Introduction – Biological foundations of Swarm Intelligence – Swarm Intelligence in Optimization – Ant Colonies: Ant Foraging Behavior – Towards Artificial Ants – Ant Colony Optimization (ACO) – SACO – Ant Colony Optimization Metaheuristic: Combinatorial Optimization – ACO Metaheuristic – Problem solving using ACO – Other Metaheuristics – Simulated annealing – Tabu Search – Local search methods – Scope of ACO algorithms.
UNIT III NATURAL TO ARTIFICIAL SYSTEMS
Biological Nervous Systems – artificial neural networks – architecture – Learning Paradigms – unsupervised learning – supervised learning – reinforcement learning – evolution of neural networks – hybrid neural systems – Biological Inspirations in problem solving – Behavior of Social Insects: Foraging –Division of Labor – Task Allocation – Cemetery Organization and Brood Sorting – Nest Building – Cooperative transport.
UNIT IV SWARM ROBOTICS
Foraging for food – Clustering of objects – Collective Prey retrieval – Scope of Swarm Robotics – Social Adaptation of Knowledge: Particle Swarm – Particle Swarm Optimization (PSO) – Particle Swarms for Dynamic Optimization Problems – Artificial Bee Colony (ABC) Optimization biologically inspired algorithms in engineering.
UNIT V CASE STUDIES
Other Swarm Intelligence algorithms: Fish Swarm – Bacteria foraging – Intelligent Water Drop Algorithms – Applications of biologically inspired algorithms in engineering. Case Studies: ACO and PSO for NP-hard problems – Routing problems – Assignment problems – Scheduling problems – Subset problems – Machine Learning Problems – Travelling Salesman problem.
COURSE OUTCOMES:
CO1: Familiarity with the basics of several biologically inspired optimization techniques.
CO2: Familiarity with the basics of several biologically inspired computing paradigms.
CO3: Ability to select an appropriate bio-inspired computing method and implement for any application and data set.
CO4: Theoretical understanding of the differences between the major bio-inspired computing methods.
CO5: Learn Other Swarm Intelligence algorithms and implement the Bio-inspired technique with other traditional algorithms.
TOTAL:45 PERIODS
TEXT BOOK
1. A. E. Elben and J. E. Smith, “Introduction to Evolutionary Computing”, Springer, 2010.
2. Floreano D. and Mattiussi C., “Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies”, MIT Press, Cambridge, MA, 2008.
3. Leandro Nunes de Castro, ” Fundamentals of Natural Computing, Basic Concepts, Algorithms and Applications”, Chapman & Hall/ CRC, Taylor and Francis Group, 2007
REFERENCES
1. Eric Bonabeau, Marco Dorigo, Guy Theraulaz, “Swarm Intelligence: From Natural to Artificial Systems”, Oxford University press, 2000.
2. Christian Blum, Daniel Merkle (Eds.), “Swarm Intelligence: Introduction and Applications”, Springer Verlag, 2008.
3. Leandro N De Castro, Fernando J Von Zuben, “Recent Developments in Biologically Inspired Computing”, Idea Group Inc., 2005.
4. Albert Y.Zomaya, “Handbook of Nature-Inspired and Innovative Computing”, Springer, 2006.
5. C. Ebelhart et al., “Swarm Intelligence”, Morgan Kaufmann, 2001.