Optimizing Large Scale Problems With Metaheuristics in a Reduced Space Mapped by Autoencoders-Application to the Wind-Hydro Coordination

dc.contributor.author Vladimiro Miranda en
dc.contributor.author Joana Maria Hora en
dc.contributor.author Palma,V en
dc.date.accessioned 2017-11-20T10:41:18Z
dc.date.available 2017-11-20T10:41:18Z
dc.date.issued 2014 en
dc.description.abstract This paper explores a technique denoted LASCA to solve large scale optimization problems with metaheuristics by reducing the search space dimension with autoassociative neural networks. The technique applies autoencoders as a reversible mapping between the original problem space and a reduced space. A metaheuristic then evolves in the latter, having its objective function assessed in the original space. The technique is illustrated with an application of an Evolutionary Particle Swarm Optimization (EPSO) algorithm to four benchmarking unconstrained optimization functions and to a wind-hydro constrained coordination problem. The new technique allows an improvement in the quality of the solutions attained. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/3564
dc.identifier.uri http://dx.doi.org/10.1109/tpwrs.2014.2317990 en
dc.language eng en
dc.relation 208 en
dc.relation 5588 en
dc.rights info:eu-repo/semantics/embargoedAccess en
dc.title Optimizing Large Scale Problems With Metaheuristics in a Reduced Space Mapped by Autoencoders-Application to the Wind-Hydro Coordination en
dc.type article en
dc.type Publication en
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