Oriane M. Neto

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The Differential Evolution (DE) algorithm was ini­ tially proposed for continuous numerical optimization, but it has been applied with success in many combinatorial optimization problems, particularly permutation-based integer combinatorial problems. In this paper, a new and general approach for combinatorial optimization is proposed using the Differential(More)
This paper presents a multiobjective approach for the design of electrical distribution networks. The objectives are defined as a monetary cost index (including installation cost and energy losses cost) and a system failure index. The true Pareto-optimal solutions are found with a multiobjective genetic algorithm that employs an efficient variable encoding(More)
The demand for different levels of Quality of Service (QoS) in IP networks is growing, mainly to attend multimedia applications. However, not only indicators of quality have conflicting features, but also the problem of determining routes covered by more than two QoS constraints is NP-complete (Nondeterministic Polynomial Time Complete). This work proposes(More)
This paper deals with multiobjective nonlinear system identification applied when modelling the relation of firing angle and equivalent reactance of a thyristor controlled series capacitor (TCSC). The mathematical representation chosen is NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs) due to its capability in modelling nonlinear(More)
The Differential Evolution (DE) algorithm is an important and powerful evolutionary optimizer in the context of continuous numerical optimization. Recently, some authors have proposed adaptations of its differential mutation mechanism to deal with combinatorial optimization, in particular permutationbased integer combinatorial problems. In this paper, the(More)
Network reconfiguration for service restoration in distribution systems is a combinatorial complex optimization problem that usually involves multiple non-linear constraints and objective functions. For large scale distribution systems no exact algorithm has found adequate restoration plans in real-time. On the other hand, the combination of Multi-Objective(More)
The achievement of approximation models may constitute a complex computational task, in the cases of models with non-linear relation between parameters and data. This problem becomes even harder when the system to be modeled is subject to noisy data, since the simple minimization of error over a training data set can give rise to misleading models that fit(More)
This paper presents a multicriterion algorithm for dealing with joint facility location and network design problems, formulated as bi-objective problems. The algorithm is composed of two modules: a multiobjective quasi-Newton algorithm, that is used to find the location of the facilities; and a multiobjective genetic algorithm, which is responsible for(More)