Optimization Techniques detailed syllabus for Artificial Intelligence & Data Science (AI&DS) for 2021 regulation curriculum has been taken from the Anna Universities official website and presented for the AI&DS students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.
For Artificial Intelligence & Data Science 6th Sem scheme and its subjects, do visit AI&DS 6th Sem 2021 regulation scheme. For Professional Elective-VII scheme and its subjects refer to AI&DS Professional Elective-VII syllabus scheme. The detailed syllabus of optimization techniques is as follows.
Course Objectives:
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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) -S-ACO – 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
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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 164
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:
- Familiarity with the basics of several biologically inspired optimization techniques.
- Familiarity with the basics of several biologically inspired computing paradigms.
- Ability to select an appropriate bio-inspired computing method and implement for any application and data set.
- Theoretical understanding of the differences between the major bio-inspired computing methods.
- Learn Other Swarm Intelligence algorithms and implement the Bio-inspired technique with other traditional algorithms.
Text Books:
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Reference Books:
- Eric Bonabeau, Marco Dorigo, Guy Theraulaz, “Swarm Intelligence: From Natural to Artificial Systems”, Oxford University press, 2000.
- Christian Blum, Daniel Merkle (Eds.), “Swarm Intelligence: Introduction and Applications”, Springer Verlag, 2008.
- Leandro N De Castro, Fernando J Von Zuben, “Recent Developments in Biologically Inspired Computing”, Idea Group Inc., 2005.
- Albert Y.Zomaya, “Handbook of Nature-Inspired and Innovative Computing”, Springer, 2006.
- C. Ebelhart et al., “Swarm Intelligence”, Morgan Kaufmann, 2001.
For detailed syllabus of all the other subjects of Artificial Intelligence & Data Science 6th Sem, visit AI&DS 6th Sem subject syllabuses for 2021 regulation.
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