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AERO 5010 Optimisation

Unit of Study Outline
Course Documents and Important Information

Unit of Study Outline

 

AERO 5010  Optimisation

(1st Semester, 3 Credits, Elective)

Course Coordinator: K. Srinivas, Luis F. Gonzalez

Course Outcomes:

The course is intended primarily to graduate students and senior undergraduate students with some background in linear algebra, and with basic knowledge of FORTRAN, C++ or Matlab. After completion of this course, students will have a much deeper understanding of methods used in modern design optimisation for linear and non-linear problems. Such problems are becoming increasingly common and important in engineering and scientific work. The course will explore the limitations, advantages and caveats associated with optimisation in engineering applications.  Students will develop their own optimisation methods for linear, non-linear, and multi-objective computational and experimental applications.

Syllabus Summary:

Introduction to design optimisation Things that can be optimised.  Problem classification.  Closed form methods.  One dimensional convex methods.  Multidimensional convex methods.  Constrained problems.  Stochastic methods Evolutionary algorithms.  Multi-objective problems and game theory Pareto, Nash and Stackelberg approaches.

Reference Books:

Press, W. H., Teukolsky, S. A., Vetterling, W. T., Flannery, B. P.  Numerical Recipes in C: The Art of Scientific Computing, Cambridge University Press, 1992.

More advanced topics covered by course notes supplied.

Classes:
Lecture:  Two 1hr sessions per week.

Tutorial:  One 1hr session per week.

Assessment:
2hr examination.
Assignments.

Course Documents and Important Information

Why Optimisation

MOO METHODS FOR MULTIDISCIPLINARY DESIGN USING PARALLEL EVOLUTIONARY ALGORITHMS, GAME THEORY AND HIERARCHICAL TOPOLOGY: THEORETICAL BACKGROUND (PART 1

MOO METHODS FOR MULTIDISCIPLINARY DESIGN USING PARALLEL EVOLUTIONARY ALGORITHMS, GAME THEORY AND HIERARCHICAL TOPOLOGY: THEORETICAL BACKGROUND (PART 2

MOO METHODS FOR MULTIDISCIPLINARY DESIGN USING PARALLEL EVOLUTIONARY ALGORITHMS, GAME THEORY AND HIERARCHICAL TOPOLOGY: THEORETICAL BACKGROUND (PART 3