Dr. Scott Ferguson

Dr. Scott Ferguson

Mechanical and Aerospace Engineering

Phone: 919-515-5231
Instructor Website
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MAE 531 Engineering Design Optimization

3 Credit Hours

Nonlinear optimization techniques with applications in various aspects of engineering design. Terminology, problem formulation, single and multiple design variables, constraints, classical and heuristic approaches, single and multiobjective problems, response surface modeling, and tradeoffs in complex engineering systems. Numerical optimization algorithms and computer-based implementation of these optimization techniques. Graduate standing in engineering and general coding skills recommended.


Graduate standing in Engineering is recommended. Calculus I and II (or equivalent).

Course Objectives

This course introduces traditional and heuristic nonlinear optimization methods that can be used to solve a wide variety of engineering design problems across all engineering disciplines. Additionally, students will study the tradeoffs associated with the design of complex engineering systems. At the end of this class, you will have the foundation needed to:

  • Model and formulate optimization problems in standard form and assess the optimality of a solution
  • Write computer code to determine the optimal solution for unconstrained and constrained nonlinear optimization problems of multiple variables
  • Determine the advantages and disadvantages of applying different optimization techniques for a specific problem
  • Model and analyze multiobjective and multidisciplinary optimization problems

Note: Programming will be required, though codes are usually simple (choice of language / software is yours)

Course Topics

  • Introduction to optimization – design variables, constraints, objective functions, penalty functions, development of formalized optimization problem statements
  • Computer implementation – both writing of code and using existing software packages – of optimization schemes with applications toward multidisciplinary and multiobjective examples
  • Techniques for solving single variable optimization problems
  • Techniques for solving constrained and unconstrained multi-variable problems
  • Modeling engineering design problems for optimization
  • Examination of heuristic-based optimization techniques
  • Mathematical foundations of multidisciplinary and multiobjective design optimization

Course Requirements

  • Homework – 30% (approximately one assignment every two weeks)
  • Exams (2) – 45% (22.5% each, a midterm and a final)
  • Project – 25%


Introduction to Optimum Design, Arora, J. S., Elsevier Academic Press, 3rd edition, ISBN 978-0-12-381375-6


Fall 2022 Coming Soon