# Vitor Nazário CoelhoOptBlocks · Research and Development

Vitor Nazário Coelho

D.Sc. in Electrical Engineering and Bachelor of Control and Automation Engineering

## About

59

Publications

21,785

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1,231

Citations

Citations since 2017

Introduction

Additional affiliations

September 2018 - present

**Autonomous researcher and developer**

Position

- Self-employed

September 2016 - August 2018

March 2015 - July 2015

## Publications

Publications (59)

Consensus mechanisms are a core feature for handling negotiation and agreements. Blockchain technology has seen the introduction of different sorts of consensus mechanism, ranging from tasks of heavy computation to the subtle mathematical proofs of Byzantine agreements. This paper presents the pioneer Delegated Byzantine Fault Tolerance (dBFT) prot...

This paper deals with Unmanned Aerial Vehicle (UAV) routing in dynamic grid scenarios with limited battery autonomy and multiple charging stations. The problem is inspired by real-world constraints, specially designed for overcoming challenges of a limited vehicle driving range. Recently, these kinds of vehicles have started to be used for deliveri...

Neighborhood search techniques are often employed to deal with combinatorial optimization problems. Previous works got good results in applying a novel neighborhood search methodology called Multi Improvement (MI). First and best improvement are classical approaches for neighborhood exploration, while the MI has emerged due to the advance of new pa...

Cities are constantly transforming and, consequently, attracting efforts from researchers and opportunities to the industry. New transportation systems are being built in order to meet sustainability and efficiency criteria, as well as being adapted to the current possibilities. Moreover, citizens are becoming aware about the power and possibilitie...

This paper discusses the application of IoT-devices with low energy consumption and high-performance computing capabilities, for processing blockchain data using Neo Virtual Machine (NeoVM). NeoVM is a turing-complete computing machine, designed for Neo Blockchain, specially designed for cryptographic and deterministic (verifiable) computation. A N...

A global trend has been motivating programmers, investors, and the academia to go towards decentralized systems. Besides providing efficient solutions for complex problems faced in our daily life, these peer-to-peer communication protocols have been promoting greater freedom and transparency. In this paper, we point out open fields for researching,...

Cities are undergoing transformations in several respects, but, mainly, regarding novel technologies. In this sense, the aim of this study is to understand the relation between the cities and the use of emerging technologies such as digital democracy, blockchain and, in particular, smart contracts. To discuss and analyze the possibilities of this e...

This paper presents an implementation of the Variable Neighborhood Search (VNS) metaheuristic for solving the optimization version of the Multidimensional Multi-Way Number Partitioning Problem (MDMWNPP). This problem consists in distributing the vectors of a given sequence into k disjoint subsets such that the sums of each subset form a set of vect...

This paper extends some explanations about the convergence of a type of Evolution Strategies guided by Neighborhood Structures, the Neighborhood Guided Evolution Strategies. Different well-known Neighborhood Structures commonly applied to Vehicle Routing Problems are used to highlight the evolution of the move operators during the evolutionary proc...

The School Timetabling Problem is widely known and it appears at the beginning of the school term of the institutions. Due to its complexity, it is usually solved by heuristic methods. In this work, we developed two algorithms based on the Variable Neighborhood Search (VNS) metaheuristic. The first one, named Skewed General Variable Neighborhood Se...

The Multi-Depot Vehicle Routing Problem (MDVRP) is a variant of the Vehicle Routing Problem (VRP) that consists in designing a set of vehicle routes to serve all customers, such that the maximum number of vehicle per depot, the vehicle capacity and the maximum time for each route are respected. The objective is to minimize the total cost of transpo...

The smart city debate will still be the scope of
business, policy-making and territorial planning in the following
years. Based on that, the paper aims to propose an interdisciplinary conceptualization of smart territories. The goal is to
promote the contrary movement of megacities creation and also
consolidate the geographical aspect of smartness....

Given the important role of machine scheduling in manufacturing industry, we discuss power consumption in sequencing jobs in a scheduling problem, assuming variable speed operation in machines. The problem involves defining the allocation of jobs to machines, the order of processing jobs and the speed of processing each job in each machine. This pr...

Image data are considered as significant data in medical systems. The amount of medical image data available for analysis keeps increasing with the modernization of image devices and biomedical image processing techniques. To prevent from being hacked over an insecure network, medical images should be encrypted safely. This study aims at proposing...

This volume presents selected, peer-reviewed, short papers that were accepted for presentation in the 5th International Conference on Variable Neighborhood Search (ICVNS'17) which was held in Ouro Preto, Brazil, during October 2-4, 2017.

This paper focus on the learning of music time series. In this context, from compressed digital audio files, we sought to verify how a song can be learned, both in terms of amplitude and frequency. Given the enormous amount of data contained in those time series, the use of classical learning methods becomes limited. Typical compressed acquisitions...

This work presents a hybrid multi-start algorithm for solving generic binary linear programs. This algorithm, called HMS, is based on a Multi-Start Metaheuristic and combines exact and heuristic strategies to address the problem. The initial solutions are generated by a strategy that applies linear programming and constraint propagation for definin...

Control lops are nowadays everywhere, from tiny devices to robust industrial applications. However, even when only few parameters are being dealt, manually fine-tuning has been shown to be a meticulous task for achieving designers’ desired performance. Fine-tuning a controller parameters is an arduous job that requests expertise of the domain. On t...

Many-objective Optimization Problems (MaOPs) present various challenges to the current optimization methods. Among these, the visualization gap is an important obstacle to the interpretation of results. Having the ability of visualizing partial or final results of a high-dimensional multi-objective problem provides key advantages to the optimizer a...

A constante evolução das cidades tem impulsionado o desenvolvimento de novas fer-ramentas para integração entre os cidadãos. Aplicações inspiradas em técnicas da pesquisa ope-racional, que auxiliam a tomada de decisão, podem tornar viáveis antigos sonhos já idealizados por filósofos. Dentre esses, destacamos sistemas judiciais mais participativos,...

Distintos são os desafios para engajar os cidadãos em decisões sociais. As Tecnologias
da Informação e Comunicação (TIC) possuem potencial para aprimorar tais relações, tornando os
sistemas das cidades mais digitais. Este trabalho investiga a utilização dessas ferramentas nas cida-
des inteligentes, do inglês Smart Cities (SC). Com o foco em otimiz...

Optimization tasks are often complex, CPU-time consuming and usually deal with finding the best (or good enough) solution among alternatives for a given problem. Parallel metaheuristics have been used in many real-world and scientific applications to efficiently solve these kind of problems. Local Search (LS) is an essential component for some meta...

Among the methods to deal with optimization tasks, parallel metaheuristics have been used in many real-world and scientific applications to efficiently solve these kind of problems. This paper presents a novel Multi Improvement strategy for dealing with the Minimum Latency Problem (MLP), an extension the classic Traveling Salesman Problem. This str...

Brain activity can be seen as a time series, in particular, electroencephalogram (EEG) can measure it over a specific time period. In this regard, brain fingerprinting can be subjected to be learned by machine learning techniques. These models have been advocated as EEG-based biometric systems. In this study, we apply a recent Hybrid Focasting Mode...

This paper focuses on Book Marketing Campaigns, where the benefit of offering each book is calculated based on a bipartite graph (biclique). A quasi Biclique problem is assessed for obtaining the probabilities of success of a given client buy a given book, considering it had received another book as free offer. The remaining optimization decision p...

This paper introduces an Unmanned Aerial Vehicle (UAV) heterogeneous fleet routing problem, dealing with vehicles limited autonomy by considering multiple charging stations and respecting operational requirements. A green routing problem is designed for overcoming difficulties that exist as a result of limited vehicle driving range. Due to the larg...

This paper deals with the container stowage planning problem, an important and a complex problem in maritime logistic optimization. The variant tackled in this work involves several constraints, inspired by real-life problems and application found in the literature. Given the complexity of the problem, which belongs to the class of \(\mathcal {NP}\...

Mini/microgrids are a potential solution being studied for future systems relying on distributed generation. Given the distributed topology of the emerging smart grid systems, different solutions have been proposed for integrating the new components ensuring communication between existing ones. The multi-agent systems paradigm has been advocated as...

As the new generation of smart sensors is evolving towards high sampling acquisitions systems, the amount of information to be handled by learning algorithms has been increasing. The Graphics Processing Unit (GPU) architecture provides a greener alternative with low energy consumption for mining big data, bringing the power of thousands of processi...

In this paper (a substantial extension of the short version presented at REM2016 on April 19–21, Maldives [1]), multi-objective power dispatching is discussed in the scope of microgrids located in smart cities. The proposed system considers the use of Plug-in Electric Vehicle (PEV) and Unmanned Aerial Vehicle (UAV) as storage units. The problem inv...

As the new generation of smart sensors is evolving towards high sampling acquisitions systems, the amount of information to be handled by learning algorithms has been increasing. The Graphics Processing Unit (GPU) architectures provide a greener alternative with low energy consumption for mining big-data, harnessing the power of thousands of proces...

In this paper, a multi-objective power dispatching problem that uses Plug-in Electric Vehicle (PEV) as storage units is considered. The problem involves several PEVs and a microgrid community, composed of small houses, residential areas, and different Renewable Energy Resources. Three different objectives are considered: microgrid total costs; usag...

The challenging problem of forecasting a given time series as accurately as possible is reality in different areas of expertise. The requirement of achieving reliable forecasts, for assisting the new generation of soft sensors, requests the development of novel smart mechanisms to be integrated into the available forecasting models. This current pa...

This paper presents an Evolution Strategy (ES) based algorithm, designed to self-adapt its mutation operators, guiding the search into the solution space using a Self-Adaptive Reduced Variable Neighborhood Search procedure. In view of the specific local search operators for each individual, the proposed population-based approach also fits into the...

Distribution planning is crucial for most companies since goods are rarely produced and consumed at the same place. Distribution costs, in addition, can be an important component of the final cost of the goods. In this paper , we study a VRP variant inspired on a real case of a large distribution company. In particular, we consider a VRP with a het...

This paper describes a multi-objective power dispatching problem that uses Plug-in Electric Vehicle (PEV) as storage units. We formulate the energy storage planning as a Mixed-Integer Linear Programming (MILP) problem, respecting PEV requirements, minimizing three different objectives and analyzing three different criteria. Two novel cost-to-variab...

This paper focuses on the targeted offers problem in direct marketing campaigns. The main objective is to maximize the feedback of customers purchases, offering products for the set of customers with the highest probability of positively accepting the offer and, at the same time, minimizing the operational costs of the campaign. Given the combinato...

This paper deals with the Unrelated Parallel Machine Scheduling Problem with Setup Times (UPMSPST). The objective is to minimize the makespan. In order to solve it, we propose a heuristic algorithm, based on Iterated Local Search (ILS), Variable Neighborhood Descent (VND) and Path Relinking (PR). In this algorithm, named AIRP, an initial solution i...

Improving the use of energy resources has been a great challenge in the last years. A new complex scenario involving a decentralized bidirectional communication between energy suppliers, distribution system and consumption is nowa-days becoming reality. Sometimes cited as the largest and most complex machine ever built, Electric Grids (EG) are been...

The importance of short-term load forecasting has been increasing lately. Electric grids are changing from a centralized single supply model towards a decentralized bidirectional grid of suppliers and consumers in an uncertain and very dynamic scenario. On the other hand, with deregulation and competition, energy price forecasting has become a big...

The importance of short-term load forecasting has been increasing lately. Electric grids are changing from a centralized single supply model towards a de-centralized bidirectional grid of suppliers and consumers in an uncertain and very dynamic scenario. On the other hand, with deregulation and competition, energy price forecasting has become a big...

This work focuses on the Total Weighted Tardiness Job-Shop Schedul-ing Problem. In this problem, each job consists of a set of tasks that must be processed on a given set of machines for uninterrupted and predetermined time. Each job has a due date and a penalty associated with the delay in comple-tion time. The objective is to minimize the total w...

This paper has its focus on the vehicle routing problem (VRP) with he-terogeneous fleet multiple trips. Given the combinatorial nature of the problem, which practically precludes the exclusive use of mathematical programming methods, said exact, solution of real cases is typically done through heuristic methods. The real vehicle routing problems in...

This work addresses the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD). Due to its complexity, we propose a heuristic algorithm for solving it, so-called GENVNS-TS-CL-PR. This algorithm combines the heuristic procedures Cheapest Insertion, Cheapest Insertion with multiple routes, GENIUS, Variable Neighborhood Search (VNS), V...

This work presents three multi-objective heuristic algorithms based on Two-phase Pareto Local Search with VNS (2PPLS-VNS), Multi-objective Variable Neighborhood Search (MOVNS) and Non-dominated Sorting Genetic Algorithm II (NSGA-II). The algorithms were applied to the open-pit-mining operational planning problem with dynamic truck allocation (OPMOP...

General Variable Neighboord Search approach apelide to the resolution of the Eternity II Puzzle

This work deals with dynamic truck allocation in open-pit-mining operational plan-ning. The objective is to optimize mineral extraction in the mines by minimizing the number of mining trucks used to meet production goals and quality requirements. According to literature, this problem is NP-hard, therefore heuristic strategies are justified. We pres...

This paper presents a hybrid algorithm based on Iterated Local Search metaheuristic. In order to test this algorithm, it is applied to a problem which requires fast answers, i.e., the open-pit mining problem. The proposed heuristic algorithm uses GRASP to generate an initial solution and has Variable Neighborhood Descent as a method of local search...

## Questions

Questions (3)

Dear researches,

I wonder know if someone knows how to plot two effects in the graph, respecting the limit of the first plot.

I already tried this:

plot(effWithoutAdapt,style="stacked",

key.args=list(space="right"),more=T,ylim=c(11,15.5), xlim=c(-1,1))

plot(effAdapt, style="stacked",rug=F, key.args=list(space="right"))

But it is not respecting the limits of the previous plot, thus it is superimposing both effects curves.

In advance, thank you very much.

Dear my friends.

I want to know the possibles ways for combining predictors from forecasting models (for example, combine different ANN models) in order to achieve a better point forecasting model, or even combine models from a population of an evolutionary algorithm.

Is there any technique for combining them optimally? For example, using some mathematical programming model, fitting the best parameters of a linear regression?

What are other options for combining these models?