| 3rd May 2021 10:57 AM | |
| Arvind Kumar | Sathyabama Institute of Science and Technology M.E. - Computer Aided Design SPRA5301 Advanced Optimization Method Syllabus Sathyabama Institute of Science and Technology M.E. - Computer Aided Design SPRA5301 Advanced Optimization Method Syllabus SATHYABAMA INSTITUTE OF SCIENCE AND TECHNOLOGY SCHOOL OF MECHANICAL ENGINEERING SPRA5301 ADVANCED OPTIMIZATION METHODS L T P Credits Total Marks 3 * 0 3 100 UNIT 1 INTRODUCTION TO OPTIMIZATION 9 Hrs. Engineering Applications of Optimization - Classification of Optimization Problems - Applications of Linear Programming – Problem Formulation-Standard Form of a Linear Programming Problem -Geometry of Linear Programming Problems- Solution by Simplex method- Sensitivity Analysis- Applications of Computer Soft wares used in Optimization problems. UNIT 2 MINIMIZATION METHODS 9 Hrs. Introduction - Unimodal Function –Elimination Methods - Unrestricted Search - Search with Fixed Step Size - Search with Accelerated Step Size - Exhaustive Search - Dichotomous Search- Interval Halving Method - Fibonacci Method - Golden Section method - Comparison of Elimination Methods. UNIT 3 DECISION ANALYSIS 9 Hrs. Decision Trees, Utility theory, Multi Objective Optimization, MCDM - Analytic Hierarchy Process (AHP), Analytic Network Process (ANP), Dynamic Programming - Multistage Decision Processes. UNIT 4 UNCONSTRAINED OPTIMIZATION METHODS 9 Hrs. Multi variable unconstrained optimization techniques: Direct search methods: Random search method univariate method, pattern search method, rosenbrock's method of rotating coordinate, steepest descent method and Conjugate gradient method. UNIT 5 HEURISTIC ALGORITHM 9 Hrs. Genetic Algorithms, Simulated Annealing, Neural Network, Optimization using fuzzy systems, Tabu Search and Scatter Search, Ant colony algorithm, Multi Response optimization - Gray Relational Analysis. Max. 45 Hrs. COURSE OUTCOMES On completion of the course, student will be able to CO1 - Feasibility study for solving an optimization problem. CO2 - Evaluate and measure the performance of an algorithm. CO3 - Understand optimization techniques using algorithms. CO4 - To design algorithms, the repetitive use of which will lead reliably to finding an approximate solution. CO5 - Describe clearly a problem, identify its parts and analyze the individual functions. CO6 - Investigate, study, develop, organize and promote innovative solutions for various applications. TEXT / REFERENCE BOOKS 1. Rao, Singaresu, S., “Engineering Optimization – Theory & Practice”, New Age International (P) Limited, New Delhi, 2000. 2. Kalyanmoy Deb, “Optimization for Engineering Design: Algorithms and Examples”, Prentice-Hall of India Private Limited, 2005. 3. Kalyanmoy Deb, “Multi-Objective Optimization Using Evolutionary Algorithms”, Wiley, 2009. 4. Lihui Wang, Amos H. C. Ng, Kalyonmoy Deb, “Multi-Objective Evolutionary Optimisation for Product Design and Manufacturing”, Springer-Verlag London Limited, 2011. 5. Ravindran – Phillips –Solberg, “Operations Research – Principles and Practice”, John Wiley India, 2006. END SEMESTER EXAMINATION QUESTION PAPER PATTERN Max. Marks: 100 Exam Duration: 3 Hrs. PART A: 5 Questions of 6 marks each – No choice 30 Marks PART B: 2 Questions from each unit of internal choice; each carrying 14 marks 70 Marks |
