Fraga, ES and Salhi, A and Talbi, E-G (2018) On the Impact of Representation and Algorithm Selection for Optimisation in Process Design: Motivating a Meta-Heuristic Framework. In: Recent Developments in Metaheuristics. Operations Research/Computer Science Interfaces (ORCS), 62 . Springer, pp. 141-149. ISBN 978-3-319-58253-5. Official URL: https://doi.org/10.1007/978-3-319-58253-5_9
Fraga, ES and Salhi, A and Talbi, E-G (2018) On the Impact of Representation and Algorithm Selection for Optimisation in Process Design: Motivating a Meta-Heuristic Framework. In: Recent Developments in Metaheuristics. Operations Research/Computer Science Interfaces (ORCS), 62 . Springer, pp. 141-149. ISBN 978-3-319-58253-5. Official URL: https://doi.org/10.1007/978-3-319-58253-5_9
Fraga, ES and Salhi, A and Talbi, E-G (2018) On the Impact of Representation and Algorithm Selection for Optimisation in Process Design: Motivating a Meta-Heuristic Framework. In: Recent Developments in Metaheuristics. Operations Research/Computer Science Interfaces (ORCS), 62 . Springer, pp. 141-149. ISBN 978-3-319-58253-5. Official URL: https://doi.org/10.1007/978-3-319-58253-5_9
Abstract
In an ideal world, it would be straightforward to identify the most suitable optimisation method to use in the solution of a given optimisation problem. However, although some methods may be more widely applicable than others, it is impossible a priori to know which method will work best. This may be due to the particular mathematical properties of the mathematical model, i.e. the formulation. It may also be due to the representation of the variables in the model. This combination of choices of method, representation and formulation makes it difficult to predict which combination may be best. This paper presents an example from process engineering, the design of heat exchanger networks, for which two different representations for the same formulation are available. Two different heuristic optimisation procedures are considered. The results demonstrate that any given combination will not lead to the best outcome across a range of case studies. This motivates the need for a multi-algorithm, multi-representation approach to optimisation, at least for process design.
Item Type: | Book Section |
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Uncontrolled Keywords: | Representation; Optimisation; Genetic algorithm; Simulated annealing; Heat exchanger networks |
Subjects: | Q Science > QA Mathematics |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Mathematics, Statistics and Actuarial Science, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 17 Nov 2017 16:46 |
Last Modified: | 16 May 2024 19:08 |
URI: | http://repository.essex.ac.uk/id/eprint/20689 |