2015
DOI: 10.1016/j.mbs.2015.08.019
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A multiobjective optimization approach for combating Aedes aegypti using chemical and biological alternated step-size control

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Cited by 18 publications
(24 citation statements)
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“…x 0,k = x(k) (15) in which N h is the time horizon in samples and x n,k are the values of the state vector at time instants n + k, with 0 ≤ n ≤ (N h − 1), when the dynamical system evolves from the initial condition x 0,k = x(k) by applying to it the input profile † It is assumed that t h is a multiple of T.…”
Section: Theoretical Backgroundmentioning
confidence: 99%
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“…x 0,k = x(k) (15) in which N h is the time horizon in samples and x n,k are the values of the state vector at time instants n + k, with 0 ≤ n ≤ (N h − 1), when the dynamical system evolves from the initial condition x 0,k = x(k) by applying to it the input profile † It is assumed that t h is a multiple of T.…”
Section: Theoretical Backgroundmentioning
confidence: 99%
“…. The optimization problem given by (15) must be solved N f times (0 ≤ k ≤ N f − 1). The essence of RHC is to use the recursive solution of a finite time optimal control problem in order to implement an infinite-time state-feedback controller in a receding horizon way.…”
Section: Theoretical Backgroundmentioning
confidence: 99%
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“…A Tabela 2 mostra os valores para os cenários obtidos empiricamente. O algoritmo genético NSGA-II foi escolhido para a busca de soluções do problema de otimização multiobjetivo deste trabalho, pela facilidade de implementação e por ser bastante conhecido na literatura para busca de soluções eficientes, como em [6]. Existem várias métricas de desempenho para verificar a qualidade de um resultado obtido a partir de um problema multiobjetivo.…”
Section: Otimização Multiobjetivounclassified