| Code |
15235
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| Year |
1
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| Semester |
S1
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| ECTS Credits |
6
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| Workload |
T(30H)/TP(30H)
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| Scientific area |
Aeronautics and Astronautics
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Entry requirements |
-
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Learning outcomes |
General objectives: to provide students with knowledge and skills on various optimization techniques, and lead them
to acquire the skills for formulating optimization problems with various restrictions and in different domains,
concerning decision making processes in the engineering area. In addition to traditional optimization techniques,
students will be able to understand and apply bio-inspired metaheuristic techniques applied to solving multi-objective
optimization problems, and to identify the advantages / disadvantages inherent to each technique.
Specific objectives: to identify optimization problems in aeronautical engineering, and to address them in a structured
way; formulate the optimization problem taking into account restrictions and domains; identify conditions of
applicability of each optimization technique; identify the appropriate optimization technique to solve each problem;
develop skills for individual and team work; prepare technical reports.
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Syllabus |
1. Introduction: Terminology, problem statement and classification of optimization problems; Optimization in engineering, multidisciplinary design, and aerospace applications; Methods and algorithms.
2. Linear Optimization: Primal and Revised Simplex Algorithms; Duality in Linear Programming; Simplex Dual and Primal-Dual Algorithms; Sensitivity Analysis.
3. Iterative and Unconstrained Optimization: Methods and algorithms . Methods .
4. Gradient-based optimization: Gradients and Hessians; Optimality conditions; Search direction and step size; Unconstrained and Constrained minimization methods; Sensitivity analysis.
5. Gradient-Free Optimization: Heuristic methods: Population based methods, Population-free based methods; Hybrid meta-heuristics.
6. Surrogate Models: Methods; Multi-fidelity approximations.
7. MDO: Complex engineering problems; Problems formulation; Models; Multidisciplinary Analysis; Level of Fidelity; Decomposition Architectures; Decision Support.
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Main Bibliography |
01. Gamboa. P.V. (2024), Notes of the curricular unit – Optimization in Engineering (Chapters 1, 5, 6 and 7), ~200 slides, UBI.
02. T. Martins, J.R.R.A., Ning, A. (2021) Engineering Design Optimization.
03. Cottle R, Thapa M (2018) Linear and Nonlinear Optimization, SpringerVerlag.
04. Papageorgiou A, Tarkian M, Amadori K, Ölvander J (2018) Multidisciplinary Design Optimization of Aerial Vehicles: A Review of Recent Advancements, International Journal of Aerospace Engineering.
05. Gandomi H, Talatahari S, Yang X-S, Alavi A (2013) Metaheuristic Applications in Structures and Infrastructures, Elsevier.
06. Yang X-S (2010) Nature-Inspired Metaheuristic Algorithms, Luniver.
07. Watson L (2008) Multidisciplinary Design Optimization. In: Floudas C, Pardalos P Encyclopedia of Optimization. Springer.
08. Engelbrecht A (2007) Computational Intelligence, An Introduction, 2ª ed., John Wiley & Sons.
09. Nocedal J, Wright S (2006) Numerical optimization, Springer.
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Teaching Methodologies and Assessment Criteria |
This course is structured primarily as a theoretical-practical unit: theoretical lectures (30 hours) and theoretical-practical classes (30 hours). In the classes, the material is presented orally with the support of multimedia slide projections, with additional information written on the board, with the resolution of exercises and with the analysis of case studies. Various supporting documents are also distributed to the students.
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Language |
Portuguese. Tutorial support is available in English.
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