Alexandre C. B. Delbem

Main chain representation for evolutionary algorithms applied to distribution system reconfiguration

Distribution system problems, such as planning, loss minimization, and energy restoration, usually involve network reconfiguration procedures. The determination of an optimal network configuration is, in general, a combinatorial optimization problem. Several Evolutionary Algorithms (EAs) have been proposed to deal with this complex problem. Encouraging results have been achieved by using such approaches. However, the running time may be very high or even prohibitive in applications of EAs to large-scale networks. This limitation may be critical for problems requiring online solutions. The performance obtained by EAs for network reconfiguration is drastically affected by the adopted computational tree representation. Inadequate representations may drastically reduce the algorithm performance. Thus, the employed representation for chromosome encoding and the corresponding operators are very important for the performance achieved. An efficient data structure for tree representation may significantly increase the performance of evolutionary-based approaches for network reconfiguration problems. The present paper proposes a tree encoding and two genetic operators to improve the EA performance for network reconfiguration problems. The corresponding EA approach was applied to reconfigure large-scale systems. The performance achieved suggests that the proposed methodology can provide an efficient alternative for reconfiguration problems.

Node-Depth Encoding and Multiobjective Evolutionary Algorithm Applied to Large-Scale Distribution System Reconfiguration

The power loss reduction in distribution systems (DSs) is a nonlinear and multiobjective problem. Service restoration in DSs is even computationally hard since it additionally requires a solution in real-time. Both DS problems are computationally complex. For large-scale networks, the usual problem formulation has thousands of constraint equations. The node-depth encoding (NDE) enables a modeling of DSs problems that eliminates several constraint equations from the usual formulation, making the problem solution simpler. On the other hand, a multiobjective evolutionary algorithm (EA) based on subpopulation tables adequately models several objectives and constraints, enabling a better exploration of the search space. The combination of the multiobjective EA with NDE (MEAN) results in the proposed approach for solving DSs problems for large-scale networks. Simulation results have shown the MEAN is able to find adequate restoration plans for a real DS with 3860 buses and 632 switches in a running time of 0.68 s. Moreover, the MEAN has shown a sublinear running time in function of the system size. Tests with networks ranging from 632 to 5166 switches indicate that the MEAN can find network configurations corresponding to a power loss reduction of 27.64% for very large networks requiring relatively low running time.

Node-Depth Encoding for Evolutionary Algorithms Applied to Network Design

Network design involves several areas of engineering and science. Computer networks, electrical circuits, transportation problems, and phylogenetic trees are some examples. In general, these problems are NP-Hard. In order to deal with the complexity of these problems, some alternative strategies have been proposed. Approaches using evolutionary algorithms have achieved relevant results. However, the graph encoding is critical for the performance of such approaches in network design problems. Aiming to overcome this drawback, alternative representations of spanning trees have been developed. This article proposes an encoding for generation of spanning forests by evolutionary algorithms. The proposal is evaluated for degree-constrained minimum spanning tree problem.

WeFold: A coopetition for protein structure prediction

The protein structure prediction problem continues to elude scientists. Despite the introduction of many methods, only modest gains were made over the last decade for certain classes of prediction targets. To address this challenge, a social‐media based worldwide collaborative effort, named WeFold, was undertaken by 13 labs. During the collaboration, the laboratories were simultaneously competing with each other. Here, we present the first attempt at “coopetition” in scientific research applied to the protein structure prediction and refinement problems. The coopetition was possible by allowing the participating labs to contribute different components of their protein structure prediction pipelines and create new hybrid pipelines that they tested during CASP10. This manuscript describes both successes and areas needing improvement as identified throughout the first WeFold experiment and discusses the efforts that are underway to advance this initiative. A footprint of all contributions and structures are publicly accessible at http://www.wefold.org. Proteins 2014; 82:1850–1868.

A hybrid case adaptation approach for case-based reasoning

Abstract Case-Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solutions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major challenges in Case-Based Reasoning is the development of an efficient methodology for case adaptation. The most widely used form of adaptation employs hand coded adaptation rules, which demands a significant knowledge acquisition and engineering effort. An alternative to overcome the difficulties associated with the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. We investigate the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches investigated how to automatically learn adaptation knowledge from a case base and apply it to adapt retrieved solutions. In order to verify the potential of the proposed approaches, they are experimentally compared with individual Machine Learning techniques. The results obtained indicate the potential of these approaches as an efficient approach for acquiring case adaptation knowledge. They show that the combination of Instance-Based Learning and Inductive Learning paradigms and the use of a data set of adaptation patterns yield adaptations of the retrieved solutions with high predictive accuracy.

Investigating Smart Sampling as a population initialization method for Differential Evolution in continuous problems

Recently, researches have shown that the performance of metaheuristics can be affected by population initialization. Opposition-based Differential Evolution (ODE), Quasi-Oppositional Differential Evolution (QODE), and Uniform-Quasi-Opposition Differential Evolution (UQODE) are three state-of-the-art methods that improve the performance of the Differential Evolution algorithm based on population initialization and different search strategies. In a different approach to achieve similar results, this paper presents a technique to discover promising regions in a continuous search-space of an optimization problem. Using machine-learning techniques, the algorithm named Smart Sampling (SS) finds regions with high possibility of containing a global optimum. Next, a metaheuristic can be initialized inside each region to find that optimum. SS and DE were combined (originating the SSDE algorithm) to evaluate our approach, and experiments were conducted in the same set of benchmark functions used by ODE, QODE and UQODE authors. Results have shown that the total number of function evaluations required by DE to reach the global optimum can be significantly reduced and that the success rate improves if SS is employed first. Such results are also in consonance with results from the literature, stating the importance of an adequate starting population. Moreover, SS presents better efficacy to find initial populations of superior quality when compared to the other three algorithms that employ oppositional learning. Finally and most important, the SS performance in finding promising regions is independent of the employed metaheuristic with which SS is combined, making SS suitable to improve the performance of a large variety of optimization techniques.

Energy restoration in distribution systems using multi-objective evolutionary algorithm and an efficient data structure

This paper proposes a new strategy for solving the service restoration problem in large-scale Distribution Systems (DS). Due to the presence of various conflicting objective functions and constraints, the service restoration task is a multi-objective, multi-constraint optimization problem. As a consequence, finding feasible solutions is a hard task. The proposed strategy uses a new tree encoding, called Node-depth Encoding (NDE), and a modified version of the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). Using NDE and its operators the proposed strategy generates only radial configurations without disconnected areas reducing the running time necessary to find feasible solutions. On the other hand, the use of the modified version of the NSGA-II enables an efficient exploration of the search space. The efficiency of the proposed strategy is shown using a Brazilian DS, with 3,860 buses, 635 switches, 3 substations, 23 feeders, 2 transformers of 50MVA and 1 transformer of 25MVA.

A power flow method computationally efficient for large-scale distribution systems

Due to several factors, conventional power flow does not present a good performance in solving distribution system power flow. Thus, the Backward and Forward Sweep method is the one more utilized in this kind of network, mainly in radial distribution system. In spite of there be several variants of this method, two more utilized are Current Summation Method and Power Summation Method. In this paper is proposed a data structure that guarantees better efficiency to these methods. It is known by Node-depth Encoding, and it can improve their performance. Mainly when they are utilized in large distribution networks and when is necessary to run the power flow many times. It will be presented results obtained by two methods well known at the literature and these methods with node-depth encoding. The results guarantee that it can be utilized in several applications, even though in larger distribution systems.

Multiobjective Evolutionary Algorithm With Many Tables for Purely Ab Initio Protein Structure Prediction

This article focuses on the development of an approach for ab initio protein structure prediction (PSP) without using any earlier knowledge from similar protein structures, as fragment‐based statistics or inference of secondary structures. Such an approach is called purely ab initio prediction. The article shows that well‐designed multiobjective evolutionary algorithms can predict relevant protein structures in a purely ab initio way. One challenge for purely ab initio PSP is the prediction of structures with β‐sheets. To work with such proteins, this research has also developed procedures to efficiently estimate hydrogen bond and solvation contribution energies. Considering van der Waals, electrostatic, hydrogen bond, and solvation contribution energies, the PSP is a problem with four energetic terms to be minimized. Each interaction energy term can be considered an objective of an optimization method. Combinatorial problems with four objectives have been considered too complex for the available multiobjective optimization (MOO) methods. The proposed approach, called “Multiobjective evolutionary algorithms with many tables” (MEAMT), can efficiently deal with four objectives through the combination thereof, performing a more adequate sampling of the objective space. Therefore, this method can better map the promising regions in this space, predicting structures in a purely ab initio way. In other words, MEAMT is an efficient optimization method for MOO, which explores simultaneously the search space as well as the objective space. MEAMT can predict structures with one or two domains with RMSDs comparable to values obtained by recently developed ab initio methods (GAPFCG, I‐PAES, and Quark) that use different levels of earlier knowledge.

Identifying Merge-Beneficial Software Kernels for Hardware Implementation

Data-mining over software can reveal similar patterns on software code. This can give important insights for the design of hardware cores, especially considering the benefits of the merge of software kernels and their implementation as a single hardware core. However, software codes have characteristics that make inadequate the direct use of typical data mining tools, mainly related to their large number of samples and the imprecise definition of code features for mining. Those characteristics affect negatively the performance of the most known data mining methods. To solve this problem, we propose in this paper the use of three techniques: the Normalized Compression Distance, the Neighbor Joining, and the Fast Newman algorithm. We combine these three techniques and propose a new approach for data mining of code repositories (DAMICORE). DAMICORE works with different types of code representations. Experiments reveal DAMICORE can indicate important software similarities at source code level. Specifically, merging soft-ware kernels identified by DAMICORE results in FPGA cores with size smaller than the overall hardware size needed when implementing a core for each kernel.