Danilo Sipoli Sanches

Multi-Objective Evolutionary Algorithm with Node-Depth Encoding and Strength Pareto for Service Restoration in Large-Scale Distribution Systems

The network reconfiguration for service restoration in distribution systems is a combinatorial complex optimization problem that usually involves multiple non-linear constraints and objectives functions. For large networks, no exact algorithm has found adequate restoration plans in real-time, on the other hand, Multi-objective Evolutionary Algorithms (MOEA) using the Node-depth enconding (MEAN) is able to efficiently generate adequate restorations plans for relatively large distribution systems. An MOEA for the restoration problem should provide restoration plans that satisfy the constraints and reduce the number of switching operations in situations of one fault. For diversity of real-world networks, those goals are met by improving the capacity of the MEAN to explore both the search and objective spaces. This paper proposes a new method called MEA2N with Strength Pareto table (MEA2N-STR) properly designed to restore a feeder fault in networks with significant different bus sizes: 3 860 and 15 440. The metrics R 2, R 3, Hypervolume and e-indicators were used to measure the quality of the obtained fronts.

Building a better heuristic for the traveling salesman problem: combining edge assembly crossover and partition crossover

A genetic algorithm using Edge Assemble Crossover (EAX) is one of the best heuristic solvers for large instances of the Traveling Salesman Problem. We propose using Partition Crossover to recombine solutions produced by EAX. Partition Crossover is a powerful deterministic recombination that is highly exploitive. When Partition Crossover decomposes two parents into q recombining components, partition crossover returns the best of 2q reachable offspring. If two parents are locally optimal, all of the offspring are also locally optimal in a hyperplane subspace that contains the two parents. One disadvantage of Partition Crossover, however, is that it cannot generate new edges. By contrast, the EAX operator is highly explorative; it not only inherits edges from parents, it also introduces new edges into the population of a genetic algorithm. Using both EAX and Partition Crossover together produces better performance, with improved exploitation and exploration.

Sequence motif finder using memetic algorithm

BackgroundDe novo prediction of Transcription Factor Binding Sites (TFBS) using computational methods is a difficult task and it is an important problem in Bioinformatics. The correct recognition of TFBS plays an important role in understanding the mechanisms of gene regulation and helps to develop new drugs.ResultsWe here present Memetic Framework for Motif Discovery (MFMD), an algorithm that uses semi-greedy constructive heuristics as a local optimizer. In addition, we used a hybridization of the classic genetic algorithm as a global optimizer to refine the solutions initially found. MFMD can find and classify overrepresented patterns in DNA sequences and predict their respective initial positions. MFMD performance was assessed using ChIP-seq data retrieved from the JASPAR site, promoter sequences extracted from the ABS site, and artificially generated synthetic data. The MFMD was evaluated and compared with well-known approaches in the literature, called MEME and Gibbs Motif Sampler, achieving a higher f-score in the most datasets used in this work.ConclusionsWe have developed an approach for detecting motifs in biopolymers sequences. MFMD is a freely available software that can be promising as an alternative to the development of new tools for de novo motif discovery. Its open-source software can be downloaded at https://github.com/jadermcg/mfmd.

Discovery Motifs by Evolutionary Computation

Jader C. Garbelini Federal Technological University of Paraná Department of Bioinformatics Cornélio Procópio, Brazil – 80230–901 jadermcg@gmail.com André Y. Kashiwabara Federal Technological University of Paraná Department of Bioinformatics Cornélio Procópio, Brazil – 80230–901 kashiwabara@utfpr.edu.br Danilo S. Sanches Federal Technological University of Paraná Department of Bioinformatics Cornélio Procópio, Brazil – 80230–901 danilosanches@utfpr.edu.br

Discovery Biological Motifs Using Heuristics Approaches

The identification of transcription factors binding sites (TFBS) – also called motifs – in DNA sequences is the first step to understanding how works gene regulation. Recognizing these patterns in the promoter regions of co-expressed genes is a determining key for this. Although there are several algorithms for this purpose, the problem is still far from being solved because of the great diversity of gene expression and the binding sites low specificity. State of the art algorithms have limitations, such as the high number of false positives and low accuracy for Identifying weak motifs. In this article we proposed a new approach based on memetic algorithms (DMMA) for discovery mofifs. The proposed approach was developed using evolutionary computation along with the local search algorithms simulated annealing and variable neighborhood search. To attest the algorithm ability, tests were conducted in four datasets - two real and two synthetic - and the results were compared with other approaches in the literature.

Multiobjective Discrete Differential Evolution for Service Restoration in Energy Distribution Systems

This paper presents a new multiobjective discrete differential evolution for service restoration in distribution systems. The proposed approach was compared with other five multiobjective evolutionary algorithms (MOEAs), which use Node-Depth Encoding (NDE). The proposed approach have been evaluated taking into account the switching operations necessary to find adequate restoration plans considering multiple non-linear constraints and objective functions. The MOEAs used in this paper have been employed to solve four different datasets with 3,860, 7,720, 15,440 and 30,880 buses, respectively. Simulations results have shown that proposed approach reached good solutions with low switching operations and reduced running time when compared with others MOEAs.

Multi-objective Evolutionary Algorithm with Discrete Differential Mutation Operator for Service Restoration in Large-Scale Distribution Systems

The network reconfiguration for service restoration in distribution systems is a combinatorial complex optimization problem that usually involves multiple non-linear constraints and objectives functions. For large networks, no exact algorithm has found adequate restoration plans in real-time, on the other hand, Multi-objective Evolutionary Algorithms (MOEA) using the Node-depth enconding (MEAN) is able to efficiently generate adequate restorations plans for relatively large distribution systems. This paper proposes a new approach that results from the combination of MEAN with characteristics from the mutation operator of the Differential Evolution (DE) algorithm. Simulation results have shown that the proposed approach, called MEAN-DE, properly designed to restore a feeder fault in networks with significant different bus sizes: 3,860 and 15,440. In addition, a MOEA using subproblem Decomposition and NDE (MOEA/D-NDE) was investigated. MEAN-DE has shown the best average results in relation to MEAN and MOEA/D-NDE. The metrics \(R_2\), \(R_3\), Hypervolume and \(\epsilon \)-indicators were used to measure the quality of the obtained fronts.

Automatic Tuning of PSSs and PODs Using a Parallel Differential Evolution Algorithm

Since Electric Power Systems are constantly subjected by perturbations, it is necessary to insert controllers for damping electromechanical oscillations originally from these perturbations. The Power System Stabilizer (PSS) and Power Oscillation Damper (POD) are two of the most common damping controllers used by the industry. However, just the inclusion of these controllers does not guarantee a satisfactory damping of the system, being necessary a good tune of them. This paper proposes a method for simultaneously tuning different kind of controllers considering several operation conditions at once. A differential evolution technique is used to perform the automatic tuning method proposed, with the great advantage of the parallel computing, since modern computers have more than one core. Simulation results with the benchmark test system New England/New York show the satisfactory performance of the parallel algorithm in a short running time than its non-parallel structure.

Combining subpopulation tables, non-dominated solutions and Strength Pareto of MOEAs to treat service restoration problem in large-scale distribution systems

The network reconfiguration for service restoration (SR) in distribution systems is a combinatorial complex optimization problem since it involves multiple non-linear constraints and objectives. For large networks, no exact algorithm has found adequate SR plans in real-time. On the other hand, methods combining Multi-objective Evolutionary Algorithms (MOEAs) with the Node-depth encoding (NDE) have shown to be able to efficiently generate adequate SR plans for large distribution systems (with thousands of buses and switches). This paper presents a new method that combining NDE with three MOEAs: (i) NSGA-II; (iii) SPEA 2; and (iii) a MOEA based on subpopulation tables. The idea is to obtain a method that cannot-only obtain adequate SR plans for large scale distribution systems, but can also find plans for small or large networks with similar quality. The proposed method, called MEA2N-STR, explores the space of the objectives solutions better than the other MOEAs with NDE, approximating better the Pareto-optimal front. This statement has been demonstrated by several simulations with DSs ranging from 632 to 1,277 switches.

Parallel simultaneous and coordinated tuning of PSSs using Ant Colony Optimization

Power system controllers are typically designed using trial-and-error techniques, which may require large effort and time from the part of the designer to find a satisfactory solution. This work proposes an algorithm to perform a simultaneous and coordinated tuning of the controllers (PSSs) in an automatic form. To perform this tuning, the algorithm uses an optimization technique based on ant colony metaheuristic. In addition, a parallel structure for the algorithm is proposed to minimize the computation time. Results show satisfactory performance of the tuned controllers, evidencing the effectiveness of the proposed technique. Furthermore, a significant productivity gain can be achieved if the engineer in charge of this design only supervises the automatic process, instead of performing all calculations himself/herself.