ED4093 Optimization Techniques in Design Syllabus:

ED4093 Optimization Techniques in Design Syllabus – Anna University PG Syllabus Regulation 2021

COURSE OBJECTIVES:

1. To understand the basic concepts of unconstrained optimization techniques.
2. To understand the basic concepts of constrained optimization techniques.
3. To provide the mathematical foundation of artificial neural networks and swarm intelligence for design problems.
4. To implement optimization approaches and to select appropriates solution for design application.
5. To demonstrate selected optimization algorithms commonly used in static and dynamic applications.

UNIT– I UNCONSTRAINED OPTIMIZATION TECHNIQUES

Introduction to optimum design – General principles of optimization – Problem formulation & their classifications- Single variable and multivariable optimization, Techniques of unconstrained minimization – Golden section, Random, pattern and gradient search methods – Interpolation methods.

UNIT– II CONSTRAINED OPTIMIZATION TECHNIQUES

Optimization with equality and inequality constraints-Direct methods–Indirect methods using penalty functions, Lagrange multipliers-Geometric programming.

UNIT–III ARTIFICIAL NEURAL NETWORKS AND SWARM INTELLIGENCE

Introduction–Activation functions, types of activation functions, neural network architectures, Single layer feed forward network, multi layer feed forward network, Neural network applications. Swarm intelligence-Various animal behaviors, Ant Colony optimization, Particle Swarm optimization.

UNIT– IV ADVANCED OPTIMIZATION TECHNIQUES

Multistage optimization–dynamic programming, stochastic programming Multi objective optimization Genetic algorithms and Simulated Annealing technique.

UNIT– V STATIC AND DYNAMIC APPLICATIONS

Structural applications – Design of simple truss members – Design of simple axial, transverse loaded members for minimum cost, weight – Design of shafts and torsionally loaded members – Design of springs. Dynamic Applications – Optimum design of single, two degree of freedom systems, vibration absorbers. Application in Mechanisms–Optimum design of simple link age mechanisms.

COURSE OUTCOMES:

Upon completion of this course, the students will be able to:
CO1 Formulate unconstrained optimization techniques in engineering design application.
CO2 Formulate constrained optimization techniques for various applications.
CO3 Implement neural network technique to real world design problems.
CO4 Apply genetic algorithms to combinatorial optimization problems.
CO5 Evaluate solutions by various optimization approaches for a design problem.

REFERENCES:

1. Goldberg, David. E, “Genetic Algorithms in Search, Optimization and Machine Learning”, Pearson, 2009.
2. Jang, J. S.R, Sun, C. T and Mizutani E., “Neuro-Fuzzy and Soft Computing”,PearsonEducation.2015,
3. JohnsonRay,C.,“Optimumdesignofmechanicalelements”,Wiley,2nd Edition1980.
4. KalyanmoyDeb,“OptimizationforEngineeringDesign:AlgorithmsandExamples”,PHILearning Private Limited, 2nd Edition,2012.
5.Rao Singiresu S., “Engineering Optimization – Theory and Practice”, New Age International Limited, New Delhi, 3rd Edition,2013.
6. Rajasekaran S and Vijayalakshmi Pai, G.A, “Neural Networks, Fuzzy LogicandGeneticAlgorithms”,PHI,2011