Telma Woerle de Lima

The node-depth encoding: analysis and application to the bounded-diameter minimum spanning tree problem

The node-depth encoding has elements from direct and indirect encoding for trees which encodes trees by storing the depth of nodes in a list. Node-depth encoding applies specific search operators that is a typical characteristic for direct encodings. An investigation into the bias of the initialization process and the mutation operators of the node-depth encoding shows that the initialization process has a bias to solutions with small depths and diameters, and a bias towards stars. This investigation, also, shows that the mutation operators are unbiased. The performance of node-depth encoding is investigated for the bounded-diameter minimum spanning tree problem. The results are presented for Euclidean instances presented in the literature. In contrast with the expectation, the evolutionary algorithm using the biased initialization operator does not allow evolutionary algorithms to find better solutions compared to an unbiased initialization. In comparison to other evolutionary algorithms for the bounded-diameter minimum spanning tree evolutionary algorithms using the node-depth encoding have a good performance.

Validation of a methodology for service restoration on a real Brazilian distribution system

Recently a practical and efficient methodology for service restoration in distribution systems was developed. This methodology combines Multi-objective Evolutionary Algorithms with the tree encoding named Node-Depth Encoding. In comparison with other methodologies already proposed for service restoration, the novel features of this methodology are: (i) to generate adequate service restoration plans for large scale distribution networks (networks modeling distribution systems with thousand of buses and switchers) with relatively soft computing without requiring any network simplification; and (ii) to generate service restoration plans for multiple-faults as good as for a single fault. This paper reports the experience in using that methodology to generate service restoration plans in one real distribution system of COPEL, a company of the Brazilian Electricity sector. More specifically, this paper reports: the main points of that methodology, the analysis of the service restoration plans generated by it performed by engineers of COPEL, and also some suggestions of these engineers in order to improve the methodology.

Node-depth encoding for evolutionary algorithms applied to multi-vehicle routing problem

The Multi-Vehicle routing problem (MVRP) in real time is a graph modification problem. In order to solve this kind of problems, alternative approaches have been investigated. Evolutionary Algorithms (EAs) have presented relevant results. However, these methodologies require special encoding to achieve proper performance when large graphs are considered. We propose a representation based on NDE [Delbem et al., (2004a); Delbem et al., (2004b)] for directed graphs. An EA using the proposed encoding was developed and evaluated for the MVRP.

Node-depth encoding for directed graphs

Network design involves several areas of research. Computer networks, electrical circuits and transportation problems are some examples. In order to deal with the complexity of these problems, approaches using evolutionary algorithms have been proposed for network design problems (NDPs) with relevant results. Nevertheless, the graph encoding is critical for the performance of evolutionary algorithms for NDPs. The node-depth encoding (NDE) has presented relevant results for NDPs involving undirected graphs. In this sense, this article proposes an extension of NDE for NDPs modeled by directed graphs.

Exhaustive search and Multi-objective Evolutionary algorithm for single fault service restoration in a real large-scale distribution system

A practical and efficient method for service restoration in distribution systems was developed and demonstrated on tests performed on the real and large-scale distribution system of Londrina city (Brazil). The method combines Multi-objective Evolutionary Algorithms with the tree encoding named Node-Depth Encoding and an Alarming Heuristic in order to find adequate service restoration plans for distribution systems with size from 3, 860 buses and 632 switches to 30, 880 buses and 5,166 switches, requiring running time less than 37 seconds for all the test cases. Moreover, the method requires no network simplification (as modeling a set of loads in a unique load point or using a relatively small set of switches instead of all switches) in order to generate adequate service restoration plans for those systems. This paper proposes a simple yet effective improvement by incorporating an exhaustive search as a first stage of that method, which guarantees the analysis of all possible initial service restoration plans (i.e. those requiring the minimal number of switching operations that can reconnect all the healthy out-of-service areas). Simulations results have shown the effectiveness of including the exhaustive search in that method.

Evolutionary approach to protein structure prediction with hydrophobic interactions

The Protein Structure Prediction (PSP) is to determine the proteintertiary structure from its amino acids. This paper presents the ProtPred and investigates its application. The first results showed that ProtPred is a consistent approach.

Determination of switching sequence of Service Restoration in Distribution Systems: Application and analysis on a real and large-scale radial system

It is computationally hard to solve the Service Restoration (SR) problem for large-scale Distribution Systems (DSs) without any system simplification, since this problem is combinatorial and non-linear, involving several constraints and objectives. The methodology named MEAN-MH+ES has proved able to generate feasible solutions (radial configuration attending all the operational constraints) with relatively soft computing and without requiring any network simplification in several tests performed on the real and large-scale DS of Londrina city (Brazil). The MEAN-MH+ES combines Multi-objective Evolutionary Algorithm with Node-Depth Encoding, Multiple-criteria tables, alarming Heuristic and an Exhaustive search. However, as the majority of the methodologies for solving the SR problem, the MEAN-MH+ES does not provide a feasible sequence of switching operations to reach the final configuration (the feasible solution) from the initial configuration (the configuration with the faulted areas identified and isolated). This paper proposes to incorporate a heuristic procedure into MEAN-MH+ES, which enable to provide a Feasible Sequence of Switching Operations (FSSO), that is, a switching operation sequence that generates only intermediate configurations that respect the operational constraints. The proposed heuristic procedure is confirmed on tests performed on the real and large-scale DS of Londrina city.

MULTI-OBJECTIVE EVOLUTIONARY APPROACH TO AB INITIO PROTEIN TERTIARY STRUCTURE PREDICTION

The Protein Structure Prediction (PSP) problem aims at determining protein tertiary structure from its amino acids sequence. PSP is a computationally open problem. Several methodologies have been investigated to solve it. Two main strategies have been employed to work with PSP: homology and Ab initio prediction. This paper presents a Multi-Objective Evolutionary Algorithm (MOEA) to PSP problem using an ab initio approach. The proposed MOEA uses dihedral angles and main angles of the lateral chains to model a protein structure. This article investigates advantages of multi-objective evolutionary approach and discusses about methods and other approaches to the PSP problem.

Some Modeling Issues for Protein Structure Prediction using Evolutionary Algorithms

Many essential functions for life are performed by proteins and the study of their structures yields the ability to elucidate these functions in terms of a molecular view. (Creighton, 1992; Devlin, 1997) The interest in discovering a methodology for protein structure prediction (PSP) is of great interesti on many fields including drug design and carriers, disease mechanisms, and the food industry. In this context, several in vitro methods have been applied, as X-ray crystallography and nuclear magnetic resonance. Despite their relative success, both methods have their limitations. Conversely, the knowledge of the primary sequence of the amino acids of a protein can be achieved by a relatively simpler experimental measurement. From this information, one can in principle predict the three dimensional arrangement of its atoms, which has motivated the investigation of ab initio methods combining such initial knowledge with effective models (force fields) in order to predict the spatial structure of a protein (Bonneau & Baker, 2001; Hardin et al., 2002). In fact, several computational methods for PSP are semi ab initio methodologies in the sense that they also use prior knowledge from both the sequence homology and the statistics found on protein databases [see e.g. (Miyazawa & Jernigan, 1985; Poole & Ranganathan, 2006)]. However, the use of these additional information restrict the search of protein structures that could be correctly predicted from the vast universe of proteins. This chapter focuses on the development of a pure ab initio approach for PSP, not using prior information. In this context, evolutionary algorithms (EAs) have been investigated as a search method due to their flexibility to solve complex optimization problems. Our researches on EAs applied to PSP are twofold: 1) the investigation of more appropriate modeling of the physical and chemical interactions of a protein for the purpose of an optimization algorithm; 2) the development of computationally efficient EAs for PSP. Two important modeling issues have been poorly investigated in the literature related to the optimization techniques for PSP: a) the combined effects of the effective Hamiltonians based on force fields and the solvation free energy contribution (Section 3), and b) the use of O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg