Oriane M. Neto

Electric distribution network multiobjective design using a problem-specific genetic algorithm

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 scheme and some problem-specific mutation and crossover operators. Results based on 21- and 100-bus systems are presented. The information gained from the Pareto-optimal solution set is shown to be useful for the decision-making stage of distribution network evolution planning.

Electric Distribution Network Expansion Under Load-Evolution Uncertainty Using an Immune System Inspired Algorithm

This paper addresses the problem of electric distribution network expansion under condition of uncertainty in the evolution of node loads in a time horizon. An immune-based evolutionary optimization algorithm is developed here, in order to find not only the optimal network, but also a set of suboptimal ones, for a given most probable scenario. A Monte-Carlo simulation of the future load conditions is performed, evaluating each such solution within a set of other possible scenarios. A dominance analysis is then performed in order to compare the candidate solutions, considering the objectives of: smaller infeasibility rate, smaller nominal cost, smaller mean cost and smaller fault cost. The design outcome is a network that has a satisfactory behavior under the considered scenarios. Simulation results show that the proposed approach leads to resulting networks that can be rather different from the networks that would be found via a conventional design procedure: reaching more robust performances under load evolution uncertainties

Using differential evolution for combinatorial optimization: A general approach

The Differential Evolution (DE) algorithm was initially 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 Evolution algorithm. The proposed approach aims at preserving its interesting search mechanism for discrete domains, by defining the difference between two candidate solutions as a differential list of movements in the search space. Thus, a more meaningful and general differential mutation operator for the context of combinatorial optimization problems can be produced. We discuss three alternatives for using the differential list of movements within the differential mutation operation. We present results on instances of the Traveling Salesman Problem (TSP) and the N-Queen Problem (NQP) to illustrate the adequacy of the proposed approach for combinatorial optimization.

Nonlinear Network Optimization—An Embedding Vector Space Approach

This paper proposes a normed-space vector representation of networks which allows defining evolutionary operators for network optimization that resemble continuous-space operators. These operators are employed here to build a genetic algorithm which becomes generic for the optimization of tree networks, without the requirement of any special encoding scheme. Such a genetic algorithm has been compared with several encoding-based genetic algorithms, on 25 and 50-node instances of the optimal communication spanning tree and of the quadratic minimum spanning tree, and has been shown to outperform all other algorithms in a stochastic dominance analysis. The proposed approach has also been applied to an electric power distribution network design (a multibranch problem), outperforming the results presented in a former reference (which have been obtained with an Ant Colony algorithm). The results of some landscape dispersion analysis suggest that the proposed normed-space network vector representation is analogous to some continuous-variable space dilation operations, which define favorable space coordinates for optimization.

A genetic algorithm for multiobjective training of ANFIS fuzzy networks

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 both the system structure and the noise (the phenomenon of model overfit). This paper proposes a multiobjective genetic algorithm for guiding the training of ANFIS fuzzy networks. This algorithm considers the complexity of network jointly with the error over the training set as relevant objectives, that should be minimized. Results obtained in three regression problems are presented to show the generalization capacity of models constructed with the proposed methodology.

A preliminary comparison of tree encoding schemes for evolutionary algorithms

This paper presents a comparative study of six encodings which have been used to represent trees in evolutionary algorithms. The study has been divided into two steps: 1) The encoding methods have been evaluated taking into account the time necessary to perform operations such as decoding, crossover and mutation, the feasibility of solutions after those operations, and the corresponding heritability and locality; 2) The encoding methods have been employed in a genetic algorithm to solve three different instances (with 10, 25 and 50 nodes) of the optimal communication spanning tree problem. Finally, the results obtained with each of the encodings are statistically compared using Kruskal-Wallis non-parametric tests and multiple comparisons. The results of this study provide insight into the properties of current encoding schemes for network design problems.

Bi-objective combined facility location and network design

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 finding the efficient topologies. These modules are executed in an iterative way, to make the estimation of whole Pareto set possible. The algorithm has been applied to the expansion of a real energy distribution system. The minimization of financial cost and the maximization of reliability have been considered as the design objectives in this case.

Multiobjective nonlinear system identification: a case study with thyristor controlled series capacitor (TCSC)

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 systems and in using prior information. The methodology for incorporation of prior knowledge is presented, and particular attention is given to the case of using information about resonant static response.

A New Differential Evolution Based Metaheuristic for Discrete Optimization

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 permutation-based integer combinatorial problems. In this paper, the authors propose a novel and general DE-based metaheuristic that preserves its interesting search mechanism for discrete domains by defining the difference between two candidate solutions as a list of movements in the search space. In this way, the authors produce a more meaningful and general differential mutation for the context of combinatorial optimization problems. The movements in the list can then be applied to other candidate solutions in the population as required by the differential mutation operator. This paper presents results on instances of the Travelling Salesman Problem (TSP) and the N-Queen Problem (NQP) that suggest the adequacy of the proposed approach for adapting the differential mutation to discrete optimization.

Multi-objective optimal reservoir operation

The need for the efficient operation of hidropower plants, which provides most of the electrical power consumed in Brazil, is related not only to the issue of energy conservation, but has also been highlighted by the increasing opposition to the construction of new large reservoirs, for ecological and social reasons. In this work, a multi-objective genetic algorithm is applied to problem of the optimization of a single Brazilian hydropower plant, with the objectives of increasing the net energy generation along the year and reducing the peak of demand of non-renewable energy sources. To increase the performance of the algorithm, two new formulations for the problem are proposed, with different ways of dealing with the operational constraints. In comparison with the more traditional approach, this results not only in efficiency gains, but also in an expanded Pareto front, which adds more flexibility to the system, by revealing new possible configurations of system operation.