Investigação da adição de métodos de aprendizado e programação matemática em uma arquitetura de hibridização de meta-heurísticas para problemas de otimização com decisões em múltiplos níveis
The hybridization of metaheuristics is a topic that several researchers have studied due to its potential to produce more efficient heuristics than those based on a single technique. However, hybridization is not easy, as there are several ways to operationalize it. The task becomes even more cha...
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| Formato: | doctoralThesis |
| Idioma: | pt_BR |
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Universidade Federal do Rio Grande do Norte
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| Endereço do item: | https://repositorio.ufrn.br/handle/123456789/55323 |
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| Resumo: | The hybridization of metaheuristics is a topic that several researchers have studied due to
its potential to produce more efficient heuristics than those based on a single technique.
However, hybridization is not easy, as there are several ways to operationalize it. The
task becomes even more challenging when three or more metaheuristic methods need
to hybridize or when someone wants to add Mathematical Programming methods, thus
creating “matheuristics.” Various methods have been proposed to hybridize metaheuristics,
including some techniques that automate hybridization, such as multi-agent architectures.
A few of these architectures use learning techniques, and an even smaller number deal
with matheuristics. This work extends the capabilities of the Multi-agent Architecture
for Metaheuristic Hybridization by including learning techniques and Mathematical Programming. The application of learning techniques is innovative, considering the agents’
choice of heuristics to apply at different search stages. This work proposes a new form of
hierarchical hybridization for Combinatorial Optimization problems with multiple decision
levels. The algorithmic proposals are tested on the Traveling Car Renter with Passengers
and the Cable Routing Problem in Wind Farms. These problems belong to the NP-hard
class and require decision-making at multiple levels. In the case of the Traveling Car
Renter with Passengers, there are three decision levels: route, car types, and customers’
transport demand. Cable routing in wind farms requires decisions concerning the cable
locations and the cable type used in each section. The experiments for the Traveling Car
Renter with Passengers were conducted on three classes of instances, totaling ninety-nine
test cases ranging from four to eighty cities, two to five vehicles, and ten to a hundred
forty people requiring transportation. Experiments for The Cable Routing Problem in
Wind Farms involved a set of two hundred instances. These instances are simulations of
real situations developed in collaboration with domain experts. The approaches proposed
in this work are compared to state-of-the-art algorithms for both problems. |
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