A novel deterministic approach, based on pre-search of the radial set, is proposed for optimal reconfiguration of radial systems. This type of optimization consists in finding a spanning tree that optimizes a given objective function. On the other hand, the deterministic approach means that the best solution will always be found. Due to the combinatorial nature of the problem, exhaustive evaluation of all possible solutions is generally not feasible when analyzing medium to large scale systems because of the enormous computational time required. The proposed algorithm works on a topologically equivalent reduced system to improve performance when facing this huge challenge in the optimization of radial systems, which consists in obtaining the Radial Set of feasible solutions. The proposal was tested in the optimal reconfiguration of two electrical distribution systems taken from the scientific literature, obtaining results that demonstrate a promising potential to apply the proposed algorithm in real medium-sized industrial system.

Anomaly Detection in Cyber Physical Systems (CPS) like Industrial Control Systems (ICS) presents research opportunities in different industries considering intelligent systems, mainly for Intrusion Detection Systems (IDS). This work surveys the specialized literature focusing on main published testbeds, datasets and methodologies for Anomaly Detection based on industrial process measurement data to develop and evaluate the performance of IDS applied for ICS. In this context, this work proposes a novel Deep Learning approach for an IDS based on a Long Short-Term Memory (LSTM) Neural Network. An experimental evaluation of obtained results for the HIL-based Augmented ICS (HAI) testbed demonstrate that the proposed LSTM-based IDS outperforms state-of-the-art alternative IDS based on other algorithms such as K-Nearest Neighbors (KNN), Decision Tree Classifier (DTC) and Random Forest (RF), considering performance metrics such as Accuracy (0.9996), Precision (0.9978), F1-Score (0.9978) and Recall (0.9978).

This paper describes a solution to the Vehicle Routing Problem (VRP) of a Paraguayan company, in a multi-objective context. The problem is formalized as the simultaneous minimization of three objective functions: (1) the total travel distance, (2) the total travel time, as well as (3) the number of routes used, while satisfying specific company constraints, such as: (a) serve the complete demand of each customer, (b) every route starts and ends on the same depot, which is one of the several depots of the company, and (c) workload limits for all fleet vehicles. To solve the problem, a specific algorithm based on MOACO (Multiobjective Ant Colony Optimization) was designed and implemented to obtain a set of Pareto solutions. The results were validated by comparing these Pareto solutions with the company's current planning, based on the experience of experts who plan the company's logistics. Experimental results confirm that in most of the studied cases, the proposed method manages to find solutions that dominate the one adopted by the company, i.e. they are better even considering all objective functions simultaneously, as shown in 2 case studies analyzed in this paper, one of which allows cost savings exceeding 40%.

In this paper, a multi-objective mathematical model is presented for the parcel distribution problem in the last mile, using properly located lockers considering four objectives: (1) initial investment, (2) monthly income, (3) satisfied customers and (4) unsatisfied demand. In order to solve the mathematical model computationally, two alternatives are implemented: an exhaustive search method and the multi-objective evolutionary algorithm NSGA-II. Simulations are presented for the city of Asuncion Paraguay, validating the model.

In the present work the problem of Optimal Placement of remote controlled Switches in an Electric Power Distribution Systems is analyzed with a Multi-objective approach. The selected objectives consider economic, technical, operational and social aspects. Real data from Paraguayan East Region is used. For the evaluation of solutions, an evaluating function based on the Monte Carlo method is implemented, which estimates the defined indices through the simulation of failures in the network, whose probabilities are obtained using the Reliability Block Diagram method. Six different multi-objective algorithms were implemented and compared using ONVGR and Hypervolume metrics, having the Multi-objective Ant Colony Optimization algorithms the best performance.

Quantum key distribution has been the focus of an important number of studies due to benefits that a widespread adoption would bring to the ever-changing technological world. For this reason, an effective way to organize the available literature on the topic so as to identify key components as well as open challenges, is needed. The present work, upon analyzing the literature, proposes a taxonomy which tackles two different approaches, one being theory-focused whereas the other considers a practical scenario with the purpose of providing an overview of the structure followed. Furthermore, after a description of the well-known BB84 protocol, a brief review of some advancements is provided. Finally, the proposed taxonomy is utilized to classify related works, bringing to light some of the open challenges that are yet to be fully developed.

The multiobjective optimization of a direct current rotary dryer that operates with pozzolan is detailed in this article. Three objective functions in steady state are minimized using a NSGA-II algorithm, these are: (1) moisture at the exit of the rotary dryer, (2) heat released to the environment through the dryer and (3) operating costs of the production process. Furthermore, the optimum operating conditions of the drying process are obtained and compared with the real process. Experimental results prove the ability of the proposed algorithm to decrease the moisture content of pozzolana by 28%, the heat released to the environment by 38% and the operating costs by 52%.

This work presents the implementation of a Resource Optimizer System for a commercial datacenter in Paraguay, which offers cloud services and uses Citrix Hypervisor (XenServer) as virtualization platform. To solve the VMP problem in a real environment, an automatic data collection module was implemented to periodically obtain the status of the datacenter resources. An especially design multi-objective memetic algorithm was also implemented to recommend improvements on the virtual machine placement, when needed. Three objective functions were considered: (1) power consumption minimization, (2) network traffic minimization and (3) economical revenue maximization. Finally, recommendations were applied in the datacenter and measurements of the used resources were made, minimizing power consumption at 29%, minimizing network traffic up to 50% while maintaining to maximum the economical revenue.

Cloud computing providers must support requests for resources in dynamic environments, considering service elasticity and overbooking of physical resources. Due to the randomness of requests, Virtual Machine Placement (VMP) problems should be formulated under uncertainty. In this context, a renewed formulation of the VMP problem is presented, considering the optimization of four objective functions: (i) power consumption, (ii) economical revenue, (iii) resource utilization and (iv) reconfiguration time. To solve the presented formulation, a two-phase optimization scheme is considered, composed by an online incremental VMP phase (iVMP) and an offline VMP reconfiguration (VMPr) phase. An experimental evaluation of five algorithms taking into account 400 different scenarios was performed, considering three VMPr Triggering and two VMPr Recovering methods as well as three VMPr resolution alternatives. Experimental results indicate which algorithm outperformed the other evaluated algorithms, improving the quality of solutions in a scenario-based uncertainty model considering the following evaluation criteria: (i) average, (ii) maximum and (iii) minimum objective function costs.

Current trends of urbanization and growing economies bring with them rising levels of traffic congestion and city governments must recur to new strategies to deal with such problems. Multi-level distribution is an already-known strategy employed by businesses, and the classic formulation of the Two-Echelon Vehicle Routing Problem (2E-VRP) reflects the perspective of a single provider, without regarding the routing decisions of other parties. The lack of coordination between providers executing their individual schedules and, consequently, the lack of a holistic approach to urban traffic may cause further problems. Additionally, the various stakeholders (government, businesses, residents) may have conflicting objectives. The main contribution of this paper is a first-time multi-objective formulation of the multi-provider (or multi-commodity) heterogeneous vehicle 2E-VRP, from a city government perspective within an Urban Goods Movement context, demonstrating with didactic examples the potential benefit of this approach to all parties involved, simultaneously considering potentially conflicting objectives.

In general, Multiobjective Optimization Problems (MOPs) with conflicting objectives requires a Decision Maker to select a solution from a set of alternatives in the Pareto Front. A visual approach is a valuable alternative to analyze the several options that may exist. Thus, to support the visual analysis, this work proposes to combine a clustering method based on the shape of the solutions together with the parallel coordinate graph. The shape of a solution is defined as the positions of the ordered values of the normalized objectives that correspond to a solution. The method proposed in this work, allows exploring groups of solutions with similar shapes, to generate visual summaries with a high degree of representativeness, and to calculate statistical data of the groups and to identify the solutions. Also, this paper presents a software tool called Tava. This tool was developed to perform the visual analysis interactively.

Chagas disease is a parasitic disease, endemic in South America. As of today, there is no effective treatment in its chronic stage. We have recently identified 134 FDA approved drugs with potential antitrypanosomal activity. In this paper, we propose a novel method for selecting combinations of drugs (drug cocktails), to provide a more effective treatment against Chagas disease. We define three measures to evaluate the predicted performance of a cocktail, establishing in this way a mathematical foundation for its analysis. This allows us to model the drug cocktail selection as a multi-objective optimisation problem, that we show can be solved efficiently with state-of-the-art evolutionary algorithms. Our analysis retrieves 57 drug cocktails containing between 2 and 6 drugs. We discuss the improvement of the cocktail selection given by our method, and the application of this approach to the identification of cocktails against other parasitic diseases.

The Maximum Diversity (MD) problem is the process of selecting a subset of elements where the diversity among selected elements is maximized. Several diversity measures were already studied in the literature, optimizing the problem considered in a pure mono-objective approach. This work presents for the first time multi-objective approaches for the MD problem, considering the simultaneous optimization of the following five diversity measures: (i) Max-Sum, (ii) Max-Min, (iii) Max-MinSum, (iv) Min-Diff and (v) Min-P-center. Two different optimization models are proposed: (i) Multi-Objective Maximum Diversity (MMD) model, where the number of elements to be selected is defined a-priori, and (ii) Multi-Objective Maximum Average Diversity (MMAD) model, where the number of elements to be selected is also a decision variable. To solve the formulated problems, a Multi-Objective Evolutionary Algorithm (MOEA) is presented. Experimental results demonstrate that the proposed MOEA found good quality solutions, i.e. between 89.20% and 99.92% of the optimal Pareto front when considering the hyper-volume for comparison purposes.

Quantum Computing is a field of study in computer science based on the laws of quantum physics.Quantum computing is an attractive subject considering that quantum algorithms proved to be more efficient than classical algorithms and the advent of large-scale quantum computation.In particular, Grover's search algorithm is a quantum algorithm that is asymptotically faster than any classical search algorithm and it is relevant for the design of fast optimization algorithms.This article proposes two algorithms based on Grover's adaptative search for biobjective optimization problems where access to the objective functions is given via two different quantum oracles.The proposed algorithms, considering both types of oracles, are compared against NSGA-II, a highly cited multiobjective optimization evolutionary algorithm.Experimental evidence suggests that the quantum optimization methods proposed in this work are at least as effective as NSGA-II in average, considering an equal number of executions.Experimental results showed which oracle required less iterations for similar effectiveness.

The linear nearest neighbor (LNN) restriction, present in several current implementations of 1D and 2D quantum circuits, limits the interaction of qubits to those which are adjacent to each other. While there have been several proposals to optimize the process of achieving LNN compliance in 1D circuits, there are few proposals for 2D circuits. Here, we present a new perspective on this problem for 2D quantum circuits. We propose to see this as a multiobjective optimization problem with two objectives: minimizing the size of the 2D grid in the circuit, and minimizing the number of SWAP gates required to achieve LNN compliance. We present some preliminary results which show that these are two contradictory objectives. Since this is common in multiobjective problems, these results indicate that a multiobjective algorithm might be a suitable way to address this problem, since it would make considerations which currently available methods do not make.

Cloud computing datacenters provide millions of virtual machines (VMs) in actual cloud markets. Nowadays, efficient location of these VMs into available physical machines (PMs) represents a research challenge, considering the large number of existing formulations and optimization criteria. Several techniques have been studied for the Virtual Machine Placement (VMP) problem. However, each article performs experiments with different datasets, making difficult the comparison between different formulations and solution techniques. Considering the absence of a highly recognized and accepted benchmark to study the VMP problem, this work proposes and implements a Workload Generator to enable the generation of different instances of the VMP problem for cloud computing environments, based on different configurable parameters. Additionally, this work also provides a set of pre-generated instances of the VMP that facilitates the comparison of different solution techniques of the VMP problem for the most diverse dynamic environments identified in the state-of-the-art.

Cloud Service Brokers (CSBs) may abstract complex resource allocation decisions for efficiently mapping demands of tenants into offers of providers. Nowadays, both demands and offers could be considered in dynamic environments, representing particular challenges in cloud computing markets. This work studies a broker-oriented Virtual Machine Placement (VMP) in dynamic environments such as: (1) variable resource offers and (2) pricing, from providers and (3) dynamic requirements of tenants. A genetic algorithm is proposed for an efficient and scalable resolution of the considered problem. Experimental results demonstrate good quality of solutions obtained by the proposed algorithm when compared to a state-of-the-art Integer Linear Programming (ILP) algorithm. Additionally, experimental results also demonstrate the good level of scalability of the proposed algorithm for large instances of the considered problem.

Nowadays, cloud computing providers offer idle resources through an auction-based system in order to maximize resource utilization and economical revenue. Cloud computing consumers have the opportunity to take advantage of the resources offered at very low spot price in exchange for lower reliability in the provision of these resources. In this context, the Spot Price Prediction (SPP) is a well studied problem mainly formulated as a time series prediction, with particularities of auction-based cloud markets. This work presents a comparative evaluation of three different well-known prediction algorithms, applied for the first time to the SPP problem, against astate-of-the-art Three-Layer Perceptron (TLP) algorithm. In order to measure the accuracy of the evaluated algorithms, the following error metrics were considered: (1) Mean-Squared Error (2) Maximum Positive Error and (3) Mean Positive Error. Experimental results indicate that the Support Vector Poly Kernel Regression (SMOReg) algorithm outperforms other evaluated algorithms for the SPP problem, improving probabilities of obtaining resources in a highly dynamic spot market.

This work proposed online formulations for the Virtual Machine Placement (VMP) problem from the providers' perspective, identifying the following dynamic parameters: . resource capacities of virtual machines (VMs) (vertical elasticity); . number of VMs of a service (horizontal elasticity); . utilization of resources of VMs (related to overbooking). Consequently, dynamic environments for online formulations of the provider-oriented VMP problem may be classified by one or more of the following classification criteria: (1) elasticity (vertical and horizontal) and (2) overbooking (server and network resources overbooking).

Cloud computing data enters dynamically provide millions of virtual machines (VMs) in actual cloud markets. In this context, Virtual Machine Placement (VMP) is one of the most challenging problems in cloud infrastructure management, considering the large number of possible optimization criteria and different formulations that could be studied. VMP literature include relevant research topics such as energy efficiency, Service Level Agreement (SLA), Quality of Service (QoS), cloud service pricing schemes and carbon dioxide emissions, all of them with high economical and ecological impact. This work classifies an extensive up-to-date survey of the most relevant VMP literature proposing a novel taxonomy in order to identify research opportunities and define a general vision on this research area.

Network Virtualization is a key technology for the Future Internet, as it allows the deployment of independent virtual networks using resources of the same basic infrastructure. An important challenge in the dynamic provision of virtual networks resides in the optimal assignment of physical resources (nodes and links) to requirements of virtual networks. This problem is known as Virtual Network Embedding (VNE). For the resolution of this problem, previous research has focused on designing algorithms based on the optimization of only one objective. On the contrary, in this work we present a multi-objective algorithm called VNE-MO-ILP for solving dynamic VNE problem, which calculates an approximation of the Pareto Front considering simultaneously resource utilization and load balancing. Results of experiments, using a network simulator, probe that the proposed algorithm is better or at least comparable to the state-of-the-art algorithm.

The Virtual Optical Network Embedding problem, also called VONE, deals with the efficient mapping of virtual resources onto opticals networks. This study proposes a heuristic algorithm called Sd-Mapping to resolve the VONE problem using traffic grooming techniques to carry several lower traffic requirements onto a single wavelength. The proposed algorithm it is compared with an reference algorithm of the literature with the following four metrics: Number of used wavelengths, Weighted number of lightpaths, Average number of physical hops and Grooming ports usage. The experimentals results show that the proposed algorithm can be regarded as a valid alternative based on promising results obtained in the following metrics over three test networks: Number of wavelengths, Weighted number of lightpaths and Average number of physical hops. In addition, this work compares five different grooming policies and the conclusion of this analysis is that the policy that minimizes the number of physical hops (MinThp) is the preferred policy considering the four metrics simultaneously.

Amazon Web Services marketizes its idle computing resources through its spot instances offer. These resources are offered through an auction-based scheme at extremely low prices. A running instance can be shutdown whenever the spot price rises above the user bid. Several challenges and opportunities emerge from this new computing paradigm. This work proposes for the first time a taxonomy on auction-based cloud computing resource provisioning, based on the study of the most relevant literature. The studied works are classified according to: (1) provider or user perspective, (2) problem solved, (3) optimization approach, (4) objective functions and (5) solution techniques. Finally, new prospective research subjects are identified and proposed for this promising research area.

This paper presents a new many-objective formulation of the Routing and Wavelength Assignment (RWA) problem in wavelength-routed optical WDM networks with protection, considering Quality of Service (QoS). A modification of the Multi-Objective Ant Colony System (MOACS) algorithm is proposed to solve the problem. Experimental results show that the proposed approach is a promising alternative compared to the original MOACS. The proposed algorithm is also compared to a Mixed Integer Linear Programming (MILP) implementation proving the results obtained are pretty closed, with a significant decrease in runtime.

In the last decades, a large number of metrics has been proposed to compare the performance of different evolutionary approaches in multi-objective optimization. This situation leads to difficulties when comparisons among the output of different algorithms are needed and appropriate metrics must be selected to perform those comparisons. Hence, no complete agreement on what metrics should be used exists. This paper presents a review and analysis of 54 multi-objective-optimization metrics in the specialized literature, discussing the usage, tendency and advantages/disadvantages of the most cited ones in order to give researchers enough information when choosing metrics is necessary. The review process performed in this work indicates that the hypervolume is the most used metric, followed by the generational distance, the epsilon indicator and the inverted generational distance.

The difficulty to solve problems with many, possibly conflicting, objectives logically increases with the number of objectives, what makes them difficult to solve using multi-objective algorithms like the well known NSGA-II. Therefore, this work proposes the use of a particle swarm optimization (PSO) algorithm to solve many-objective problems. The main premise of this work is that MOPSO may be a good option for solving many-objective problems, presenting experimental evidence that supports this premise using the well known DTLZ benchmark with different performance metrics such as hypervolume, coverage and generational distance, among others.

The process of selecting which virtual machines should be located (i.e. executed) at each physical machine of a datacenter is commonly known as Virtual Machine Placement (VMP). This work presents a general manyobjective optimization framework that is able to consider as many objective functions as needed when solving the VMP problem in a pure multi-objective context. As an example of utilization of the proposed framework, for the first time a formulation of the many-objective VMP problem (MaVMP) is proposed, considering the simultaneous optimization of the following five objective functions: (1) power consumption, (2) network traffic, (3) economical revenue, (4) quality of service and (5) network load balancing. To solve the formulated many-objective VMP problem, an interactive memetic algorithm is proposed. Simulations prove the correctness of the proposed algorithm and its effectiveness converging to a treatable number of solutions in different experimental scenarios.

This work presents implementation details of the Parareal method using PETSc in a distributed and multicore architecture, which is used for the resolution of a parabolic optimal control problem. To this end, this optimization problem is discretized yielding a large KKT linear system. In the context of this work, the Parareal method allows not only to reach problem sizes which normally can not be solved using a single computer, but also allows to speed up the computational resolution time. The implementation developed in this work offers a parallelization relative efficiency for the strong scaling of approximately 70% each time the processes count doubles, while for the weak scaling it is 75 % each time the processes count doubles for a constant solution size per process and 96% each time the processes count doubles for a constant data size per process.

This work presents a parallel implementation of the Parareal method using Portable Extensible Toolkit for Scientific Computation (PETSc).An optimal control problem of a parabolic partial differential equation with known boundary conditions and initial state is solved, where the minimized cost function relates the controller v usage and the approximation of the solution y to an optimal known function y * , measured by y and y * , respectively.The equations that model the process are discretized in space using Finite Elements and in time using Finite Differences.After the discretizations, the problem is transformed to a large linear system of algebraic equations, that is solved by the Conjugate Gradient method.A Parareal preconditioner is implemented to speed up the convergence of the Conjugate Gradient.The main advantage in using the Parareal approach is to speed up the resolution time, when comparing to implementations that use only the Conjugate Gradient or GMRES methods.The implementation developed in this work offers a parallelization relative efficiency for the strong scaling of approximately 70% each time the process count doubles.For weak scaling, 75% each time the process count doubles for a constant solution size per process and 96% each time the process count doubles for a constant data size per process.

The process of selecting which virtual machines will be placed (i.e. executed) in the physical machines available in a Datacenter is known as virtual machine placement problem. This work proposes for the first time a formulation of the problem, with a multi-objective approach, of the main objective functions studied so far as mono-objective in the state of the art. Also it is proposed a multi-objective memetic algorithm for solving the proposed problem. The validity of the proposed formulation is checked by comparing experimental results of the proposed algorithm with a brute force algorithm. Finally it is experimentally verified the scalability of the used meta-heuristic.

The huge bandwidth of optical fibres is exploited through wavelength division multiplexing technology, which introduces new complexities in the routing problem. In this context, the wavelength converter allocation problem has become a key factor to minimize blocking. The wavelength converter allocation problem has been treated as a mono-objective problem minimizing the number of wavelength converters or minimizing blocking; however, both criteria are in conflict with each other. Therefore, the wavelength converter allocation problem is studied here in a pure multi-objective optimization context for more appropriate decision making. This work proposes a multi-objective optimization approach based on an evolutionary algorithm which simultaneously minimizes blocking and the number of wavelength converters. Extensive simulations on three real optical networks show promising results in the sense that our algorithm generates the trade-off curve between blocking and the number of converters needed, and outperforms a recently proposed approach.

Generalized multiobjective multitree model (GMM-model) studied for the first time multitree-multicast load balancing with splitting in a multiobjective context. To solve the GMM-model, a multiobjective evolutionary algorithm (MOEA) inspired by the strength Pareto evolutionary algorithm (SPEA) was already proposed. In this paper, we extend the GMM-model to dynamic multicast groups (i.e. egress nodes can change during the connection's lifetime), given that, if recomputed from scratch, it may consume a considerable amount of CPU time. To alleviate this drawback we propose a dynamic generalized multiobjective multitree model (dynamic-GMM-model) that in order to add new egress nodes makes use of a multicast tree previously computed with the GMM-model. To solve the dynamic-GMM-model, a new MASPA (multiobjective approximation using shortest path algorithm) heuristic is proposed. Experimental results considering up to 11 different objectives are presented for the well-known NSF network. We compare the performance of the GMM-model using MOEA with the proposed dynamic-GMM-model using MASPA, showing that reasonable good solutions may be found using fewer resources (such as memory and time). The main contributions of this paper are the optimization model for dynamic multicast routing; and the proposed heuristic algorithm.

This work presents a new version of a multiobjective multicast routing algorithm (MMA) for traffic-engineering, based on the strength Pareto evolutionary algorithm (SPEA), which simultaneously optimizes the maximum link utilization, the cost of the tree, the maximum end-to-end delay and the average delay. In this way, a set of optimal solutions, known as Pareto set, is calculated in only one run, without a priori restrictions. Simulation results show that MMA is able to find Pareto optimal solutions. They also show that for dynamic multicast routing, where the traffic requests arrive one after another, MMA outperforms other known algorithms.

AntNet is a new algorithm for packet routing in communication networks. It was first proposed by M. Dorigo and G. Di Care (1997). In AntNet, a group of mobile agents (artificial ants) build paths between pairs of nodes, exploring the network concurrently and exchanging data to update routing tables. This paper analyzes AntNet algorithms and proposes improvements, comparing their performance with respect to the original AntNet and other commercial algorithms like RIP (routing information protocol) and OSPF (open shortest path first). Simulation results indicate a better throughput (number of packets successfully routed per unit time) for the improved proposals. As for packet delay, the improved proposals bettered the original AntNet, although RIP and OSPF were unbeatable in this measure of performance. Due to the increase in the number of users in networks like the Internet, it may be expected that network service administrators will prioritize throughput to maximize services to a growing number of users. So, AntNet and its variant here proposed are promising options for routing in large public networks such as the Internet.

This paper formalizes a general technique to combine different methods in the solution of large systems of nonlinear equations using parallel asynchronous implementations on distributed-memory multiprocessor systems. Such combinations of methods, referred to as team algorithms, are evaluated as a way of obtaining desirable properties of different methods and a sufficient condition for their convergence is derived. The load flow problem of electrical power networks is presented as an example problem that, under certain conditions, has the characteristics to make a team algorithm an appealing choice for its solution. Experimental results of an implementation on an Intel iPSC/860 Hypercube are reported, showing that considerable speedup and robustness can be obtained using team algorithms.