| Code |
7144
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| Year |
1
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| Semester |
S2
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| ECTS Credits |
6
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| Workload |
T(30H)/TP(30H)
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| Scientific area |
Informatics, Automation and Control
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Entry requirements |
It is recommended, but not mandatory, that students have skills and concepts about programming, software matlab® and algorithms.
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Mode of delivery |
Face to face.
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Work placements |
Not applicable.
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Learning outcomes |
This curricular unit aims to provide the students with knowledge and skills about the various optimization techniques, as well as acquire skills in the optimization problems formulation and with restrictions in many fields of engineering. In addition to traditional optimization techniques, students will be able to understand the concept and apply the most modern bio-inspired meta-heuristic techniques; its mechanisms of intensification and diversification and identify the main advantages and disadvantages inherent to each technique.
By the end of the programming course, students have the following skills:
- Identify an optimization problem and approach it in a structured way;
- Formulate the problem and its constraints as an optimization problem;
- Identify the main properties inherent to each optimization technique;
- Identify the appropriate optimization technique to solve a given optimization problem;
- Work individually and within a team;
- Elaborate technical reports.
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Syllabus |
1. Introduction to engineering optimization problems. Gradient based optimization techniques. Particular cases with convex and non-convex optimization problems.
2. Linear Programming - Simplex Method. Non-linear programming - Quadratic programming. Concepts and applications.
3. Introduction to non-population based meta-heuristics techniques, simulated annealing, tabu search. Properties and their characteristics. Performance analysis. Implementation and application in several optimization problems.
4. Introduction to bio-Inspired population based meta-heuristics techniques, particle swarm optimization, differential evolution and other bio-inspired meta-heuristics techniques. Movement strategies (mechanisms of intensification and diversification). Control parameters and their influence on performance. Implementation and application in several optimization problems.
5. Hybrid meta-heuristics. Types of Hybridization. Collaborative and integrative structure. Implementation and application.
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Main Bibliography |
1. Andries P. Engelbrecht, Computational Intelligence, An Introduction, 2ª ed., John Wiley & Sons, 2007.
2. X.-S. Yang, Nature-Inspired Metaheuristic Algorithms, Luniver Press, 2010.
3. S.J.P.S. Mariano, Planeamento da Operação de Sistemas de energia eléctrica, Textos de apoio às aulas, Universidade da Beira Interior.
4. IEEE Xplore, Serviços de Documentação, Pesquisa on-line em bases de dados científicas.
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Language |
Portuguese. Tutorial support is available in English.
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