JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Prerequisite

Artificial Intellegence techniques in power systems

Course Objectives

to prepare students to:

• Understand evolutionary algorithms like GA,PSO, ANT COLONY and BEE COLONY etc.
• Apply evolutionary algorithms to solve power systems problems
• understand solution of Multi-Objective optimization using these algorithms

Course Outcomes

Upon the completion of this course, the student 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 conditions-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 AND 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 and 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 and BAT OPTIMIZATION ALGORITHM

Bat algorithm-Echolocation of bats-Behaviour 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

R19 M. TECH. EPE/EPS

TEXT BOOKS:

• 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. “Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization” by Muzaffar eusuff, Kevin lansey and Fayzul pasha, Engineering Optimization, Taylor & Francis, Vol. 38, No. 2, pp.129-154, March 2006.
• 2. “A New Metaheuristic Bat-Inspired Algorithm” by Xin-She Yang, Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) (Eds. J.R. Gonzalez et al.), Studies in Computational Intelligence, Springer Berlin, 284, Springer, 65-74 (2010).
• 3. “Firefly Algorithms for Multimodal Optimization” Xin-She Yang, O. Watanabe and T. Zeugmann (Eds.), Springer-Verlag Berlin Heidelberg, pp. 169-178, 2009. R19 M. TECH. EPE/EPS M. TECH. I Year II Sem. (EPE/EPS)