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

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....

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Vydané v:IEEE transactions on power systems Ročník 29; číslo 6; s. 3078 - 3085
Hlavní autori: Miranda, Vladimiro, da Hora Martins, Joana, Palma, Vera
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.11.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Autoencoders
Benchmark testing
evolutionary algorithms
Evolutionary computation
large scale optimization
Linear programming
metaheuristics
Neural networks
Optimization
Particle swarm optimization
Planning
wind-hydro coordination problem
ISSN:0885-8950, 1558-0679
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2014.2317990