JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Prerequisite

Artificial Intelligence Techniques in Electrical Engineering

Course Objectives:

• To understand Evolutionary algorithms like GA, PSO, ANT COLONY and BEE COLONY etc.
• To apply these Evolutionary algorithms to solve power systems problems
• To also able to understand solution of multi-Objective optimization using these algorithms

Course Outcomes

After completion of the course, students will be able to:

• Discriminate the capabilities of bio-inspired system and conventional methods in solving optimization problems.
• Examine the importance of exploration and exploitation of swarm intelligent system to attain near global optimal solution.
• Distinguish the functioning of various swarm intelligent systems.
• Employ various bio-inspired algorithms for power systems engineering applications.

UNIT - I

FUNDAMENTALS OF SOFT COMPUTING TECHNIQUES Definition, Classification of optimization problems, Unconstrained and constrained optimization optimality condition, Introduction to intelligent systems, Soft computing techniques, Conventional computing versus swarm computing, Classification of meta-heuristic techniques, Single solution based and population based algorithms, Exploitation and exploration in population based algorithms, Properties of Swarm intelligent Systems, Application domain, Discrete and continuous problems, Single objective and multi-objective problems.

UNIT - II

GENETIC ALGORITHM & PARTICLE SWARM OPTIMIZATION Genetic algorithms, Genetic algorithm versus Conventional Optimization Techniques, Genetic representations and selection mechanisms: Genetic operators, Different types of crossover and mutation operators, Bird flocking and Fish Schooling-anatomy of a particle, Equations based on velocity and positions, PSO topologies, Control parameters, GA and PSO algorithms for solving ELD problems.

UNIT - III

ANT COLONY OPTIMIZATION & ARTIFICIAL BEE COLONY ALGORITHMS Biological ant colony system, Artificial ants and assumptions, Stigmergic communications, Pheromone updating, Local-global-pheromone evaporation, Ant colony system, ACO models, Touring ant colony system, Max min ant system, Concept of elastic ants, Task partitioning in honey bees, Balancing foragers and receivers, Artificial bee colony (ABC) algorithms, Binary ABC algorithms, ACO and ABC algorithms for solving Economic Dispatch of thermal units.

UNIT - IV

SHUFFLED FROG-LEAPING ALGORITHM & BAT OPTIMIZATION ALGORITHM Bat algorithm, Echolocation of bats, Behavior of micro bats, Acoustics of echolocation, Movement of Virtual bats, Loudness and pulse Emission, Shuffled frog algorithm, Virtual population of frogs, Comparison of memes and genes, Memeplex formation, Memeplex updation, BA and SFLA algorithms for solving ELD and optimal placement and sizing of the DG problem.

UNIT - V

MULTI OBJECTIVE OPTIMIZATION Multi-Objective optimization introduction, Concept of pareto optimality, non-dominant sorting technique, Pareto fronts, best compromise solution, Min-max method, NSGA-II algorithm and applications to power systems.

TEXTBOOKS:

• 1. Xin-She Yang, “Recent Advances in Swarm Intelligence and Evolutionary Computation”, Springer International Publishing, Switzerland, 2015.
• 2. Kalyanmoy Deb,” Multi-Objective Optimization using Evolutionary Algorithms”, John Wiley & Sons, 2001.

REFERENCES:

• 1. James Kennedy and Russel E Eberheart, “Swarm Intelligence”, The Morgan Kaufmann Series in Evolutionary Computation, 2001.
• 2. Eric Bonabeau, Marco Dorigo and Guy Theraulaz, “Swarm Intelligence-From natural to Artificial Systems”, Oxford university Press, 1999.
• 3. David Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning”, Pearson Education, 2007.
• 4. Konstantinos E. Parsopoulos and Michael N. Vrahatis,” Particle Swarm Optimization and Intelligence: Advances and Applications”, Information Science reference, IGI Global, 2010.
• 5. N P Padhy, “Artificial Intelligence and Intelligent Systems”, Oxford University Press, 2005.

REFERENCE PAPERS:

• 1. Muzaffar eusuff, Kevin lansey and Fayzul pasha, “Shuffled frog-leaping algorithm: a memetic meta- heuristic for discrete optimization”, Engineering Optimization, Taylor & Francis, Vol. 38, No. 2, pp.129-154, March 2006.
• 2. Xin-She Yang, “A New Metaheuristic Bat-Inspired Algorithm”, Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) (Eds. J.R. Gonzalez et al.), Studies in Computational Intelligence, Springer Berlin, 284, Springer, pp. 65-74, 2010.
• 3. Xin-She Yang, O. Watanabe and T. Zeugmann, “Firefly Algorithms for Multimodal Optimization” (Eds.), Springer-Verlag Berlin Heidelberg, pp. 169-178, 2009. M. Tech – II Semester L T P C 3 0 0 3