Optimization with meta-heuristics

Recommended Prerequisites

Knowledge about optimization models and algorithms.

Domain of a programming language. 

Teaching Methods

Lectures and tutorial sessions on the above mentioned syllabus. Once the basic skills are acquired by the students, the sessions will be oriented according to the specific work to be carried out by each student, who will be defined to be potentially useful for the PhD thesis. Evaluation elements: detailed report describing the design and computational implementation of a meta-heuristic for a problem under study, analyzing and comparing the respective results for different parameterizations of the algorithm. 

Learning Outcomes

The main objective of this course unit is to provide students with advanced methodological skills in meta-heuristic approaches to deal with complex optimization problems. Different types of meta-heuristics will be introduced and problems in the management area that have been dealt with this type of techniques will be presented.

In face of complex combinatorial problems, non-linear, and/or with multiple objective functions, which are difficult to deal with exact methods, the students should identify appropriate meta-heuristic approaches, considering the model characteristics and its dimension. The students should also be able to build computational implementations of a meta-heuristic approach and apply it to a case study.

Work Placement(s)

No

Syllabus

Meta-heuristics for combinatorial and/or nonlinear optimization problems: tabu search; simulated annealing; genetic/evolutionary algorithms; particle swarm optimization; differential evolution.

Meta-heuristics for multiobjective problems, in particular evolutionary algorithms. Applications in management problems.

Head Lecturer(s)

Maria João Teixeira Gomes Alves

Assessment Methods

Assessment
Periodic or by final exam as given in the course information: 100.0%

Bibliography

- Z. Michalewicz, D. B. Fogel. "How to Solve It: Modern Heuristics", Springer, 2004.

 -  E. Talbi. "Metaheuristcs - from design to implementation”, Wiley, 2009.

 - R. Takahashi, A. G. Cunha, C. H. Antunes (Coord.). "Manual de Computação Evolutiva e Metaheurística", Imprensa da Universidade de Coimbra, 2012.