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Dynamic optimization multi-objective problems (DMOPs) are characterized by the environmental changes they experiment . For these problems, optimization algorithms have limited time to find accurate results before every change. A common scenario in optimization is the presence of a decision maker (DM), which establishes preferences on the problem being solved. Nowadays, there are few works focused on applying preference incorporation techniques in DMOPs. This work proposes the Plane Separation (PS) method, a novel technique that allows incorporating preferences in the optimization process by splitting the population into multiple planes based on the proximity of solutions to the region of interest (ROI). PS uses those planes to focus the search towards the ROI while maintaining diversity in the solutions set to avoid stagnation in local optima. PS is incorporated into two versions of DNSGA-II, and in a novel dynamic version of GDE3 we propose. These algorithms are also used as low-level heuristics of a hyper-heuristic. All PS-incorporated algorithms were compared against DRNSGA-II, a dynamic version proposal of a single-reference-point technique for solving MOPs under different preferential setups. The results favor the performance of the proposed PS-based algorithms, confirming its feasibility and effectiveness as a technique for incorporating preferences within a dynamic environment.

Content Distribution Networks (CDN) are key for providing worldwide services and content to end-users. In this work, we propose three multiobjective evolutionary algorithms for solving the problem of designing and optimizing cloud-based CDNs. We consider the objectives of minimizing the total cost of the infrastructure (including virtual machines, network, and storage) and the maximization of the quality-of-service provided to end-users. The proposed model considers a multi-tenant approach where a single cloud-based CDN is able to host multiple content providers using a resource sharing strategy. The proposed evolutionary algorithms address the offline problem of provisioning infrastructure resources while a greedy heuristic method is proposed for addressing the online problem of routing contents. The experimental evaluation of the proposed methods is performed over a set of realistic problem instances. Results indicate that the proposed approach is effective for designing and optimizing cloud-based CDNs reducing total costs by up to 10.3% while maintaining an adequate quality of service.

We propose a new method for multi-objective optimization, called Fuzzy Adaptive Multi-objective Evolutionary algorithm (FAME). It makes use of a smart operator controller that dynamically chooses the most promising variation operator to apply in the different stages of the search. This choice is guided by a fuzzy logic engine, according to the contributions of the different operators in the past. FAME also includes a novel effective density estimator with polynomial complexity, called Spatial Spread Deviation (SSD). Our proposal follows a steady-state selection scheme and includes an external archive implementing SSD to identify the candidate solutions to be removed when it becomes full. To assess the performance of our proposal, we compare FAME with a number of state of the art algorithms (MOEA/D-DE, SMEA, SMPSOhv, SMS-EMOA, and BORG) on a set of difficult problems. The results show that FAME achieves the best overall performance.

Presently, we are living in a hyper-connected world where millions of heterogeneous devices are continuously sharing information in different application contexts for wellness, improving communications, digital businesses, etc. However, the bigger the number of devices and connections are, the higher the risk of security threats in this scenario. To counteract against malicious behaviours and preserve essential security services, Network Intrusion Detection Systems (NIDSs) are the most widely used defence line in communications networks. Nevertheless, there is no standard methodology to evaluate and fairly compare NIDSs. Most of the proposals elude mentioning crucial steps regarding NIDSs validation that make their comparison hard or even impossible. This work firstly includes a comprehensive study of recent NIDSs based on machine learning approaches, concluding that almost all of them do not accomplish with what authors of this paper consider mandatory steps for a reliable comparison and evaluation of NIDSs. Secondly, a structured methodology is proposed and assessed on the UGR’16 dataset to test its suitability for addressing network attack detection problems. The guideline and steps recommended will definitively help the research community to fairly assess NIDSs, although the definitive framework is not a trivial task and, therefore, some extra effort should still be made to improve its understandability and usability further., The authors would like to acknowledge the Spanish Ministerio de Ciencia, Innovación y Universidades and ERDF for the support provided under contracts RTI2018-100754-B-I00 (iSUN) and RTI2018-098160-B-I00 (DEEPAPFORE).

Several previous works have shown how using prior knowledge within machine learning models helps to overcome the curse of dimensionality issue in high dimensional settings. However, most of these works are based on simple linear models (or variations) or do make the assumption of knowing a pre-defined variable grouping structure in advance, something that will not always be possible. This paper presents a hybrid genetic algorithm and machine learning approach which aims to learn variables grouping structure during the model estimation process, thus taking advantage of the benefits introduced by models based on problem-specific information but with no requirement of having a priory any information about variables structure. This approach has been tested on four synthetic datasets and its performance has been compared against two well-known reference models (LASSO and Group-LASSO). The results of the analysis showed how that the proposed approach, called GAGL, considerably outperformed LASSO and performed as well as Group-LASSO in high dimensional settings, with the added benefit of learning the variables grouping structure from data instead of requiring this information a priory before estimating the model.

Today's IT systems and IT processes must be ready to handle change in an efficient and responsive manner to allow businesses to both evolve and adapt to a changing world. In this paper we describe an approach that consists of using simulation based multi-objective optimization to select optimal ITIL change management process strategies that help IT managers achieve process efficiency as a Critical Success Factor (CSF). A multi-method simulation model, which is based on agent-based and discrete-event simulation paradigms, has been built to simulate the whole process lifecycle, since the change initiation until its closure. As most engineering problems, assuring an efficient delivery of the change management process requires optimizing simultaneously the corresponding Key Performance Indicators (KPIs) in which the process-efficiency CSF can be rolled down. In this paper, we show the results of applying two well-known Multi-Objective Evolutionary Algorithms, namely NSGA-II and SPEA2, to obtain a set of optimal solutions for the KPIs associated with delivering process efficiency as a CSF. We also compare the results obtained with the output from the single-objective optimization algorithm provided by the simulation tool. The experimental work included shows how the approach can provide the IT manager with a wide range of high quality solutions to support them in their decision-making towards CSF achievement.

We present a novel method to automatically generate new parallel solvers for optimization problems, called the Virtual Savant. It applies machine learning to model a reference algorithm (which is treated as a black box) from its solutions to a given problem, and after, it is able to efficiently and accurately reproduce its solutions on new unseen problem instances. Additionally, the generated parallel algorithm is scalable to a different problem dimension. We analyze the performance and accuracy of our novel technique on a scheduling problem, and we use instances of different features and sizes. Virtual Savant was used to learn from an exact algorithm, a well-known heuristic, and a specialized parallel metaheuristic. During our experiments, our method solved to optimality up to 95.5% of the 200 unseen instances tested. For larger instances, it is not only highly competitive with the reference algorithms, but it is even able to outperform them in some cases. Another outstanding result is that Virtual Savant improved its accuracy when scaling the problem size (without additional training), with respect to the original algorithm.

We present a parallel multi-objective cooperative coevolutionary variant of the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm. The algorithm, called CCSMPSO, is the first multi-objective cooperative coevolutionary algorithm based on PSO in the literature. SMPSO adopts a strategy for limiting the velocity of the particles that prevents them from having erratic movements. This characteristic provides the algorithm with a high degree of reliability. In order to demonstrate the effectiveness of CCSMPSO, we compare our work with the original SMPSO and three different state-of-the-art multi-objective CC metaheuristics, namely CCNSGA-II, CCSPEA2 and CCMOCell, along with their original sequential counterparts. Our experiments indicate that our proposed solution, CCSMPSO, offers significant computational speedups, a higher convergence speed and better or comparable results in terms of solution quality, when evaluated against three other CC algorithms and four state-of-the-art optimizers (namely SMPSO, NSGA-II, SPEA2, and MOCell), respectively. We then provide a scalability analysis, which consists of two studies. First, we analyze how the algorithms scale when varying the problem size, i.e., the number of variables. Second, we analyze their scalability in terms of parallelization, i.e., the impact of using more computational cores on the quality of solutions and on the execution time of the algorithms. Three different criteria are used for making the comparisons, namely the quality of the resulting approximation sets, average computational time and the convergence speed to the Pareto front.

This article presents how Virtual Savant (VS) can be used to automatically learn, from an exact algorithm, how to solve the basic independent Next Release Problem in a quick and accurate way. This variant of the Next Release Problem (NRP) is in essence a 0/1 Knapsack Problem and VS is applied to solve the underlying optimization problem. VS is a generic problem-solving approach based on machine learning and heuristics, that works by mimicking how a reference program produces solutions to problem instances. Essentially, VS learns how to generate solutions to a given problem from a reference algorithm and a training set of instances, and it is able to efficiently solve new problem instances by using the acquired knowledge. In this paper, an exact optimizer is used as a reference algorithm. Hence, we are using VS to learn from optimal solutions, which helps to reduce the approximation error inherent to the learning process. We compare five versions of VS (differing in the heuristics they implement) on a large benchmark, composed of problems with different sizes and difficulties. For the best VS configuration, which also has the lowest computational complexity, computed solutions differ less than 1% from the optima in the worst case. Therefore, VS succeeds in learning how to solve the problem under study, and it does so in a highly efficient way, exempting the programmer from having a deep knowledge of the problem domain or highly specialized parallel programming skills.

It is important to know the properties of an optimization problem and the difficulty an algorithm faces to solve it. Population evolvability obtains information related to both elements by analysing the probability of an algorithm to improve current solutions and the degree of those improvements. DPEM_HH is a dynamic multi-objective hyper-heuristic that uses low-level heuristic (LLH) selection methods that apply population evolvability. DPEM_HH uses dynamic multiobjective evolutionary algorithms (DMOEAs) as LLHs to solve dynamic multi-objective optimization problems (DMOPs). Population evolvability is incorporated in DPEM_HH by calculating it on each candidate DMOEA for a set of sampled generations after a change is detected, using those values to select which LLH will be applied. DPEM_HH is tested on multiple DMOPs with dynamic properties that provide several challenges. Experimental results show that DPEM_HH with a greedy LLH selection method that uses average population evolvability values can produce solutions closer to the Pareto optimal front with equal to or better diversity than previously proposed heuristics. This shows the effectiveness and feasibility of the application of population evolvability on hyperheuristics to solve dynamic optimization problems. First published online 12 July 2019

This work addresses the multi-objective resource provisioning problem for building cloud-based CDNs. The optimization objectives are the minimization of VM, network and storage cost, and the maximization of the QoS for the end-user. A brokering model is proposed such that a single cloud-based CDN is able to host multiple content providers applying a resource sharing strategy. Following this model, an offline multiobjective evolutionary approach is applied to optimize resource provisioning while a greedy heuristic is proposed for addressing online routing of content. Experimental results indicate the proposed approach may reduce total costs by up to 10.6% while maintaining high QoS values.

We propose a new accurate Micro Genetic Algorithm ( μ GA) for multi-objective optimization problems that we call Micro-FAME (or μ FAME). The distinctive feature of μ FAME with respect to the other existing multi-objective algorithms in the literature is its high elitism and fast convergence, produced by the application of the evolution directly on the Pareto front approximation. This means that it has a bounded population of variable size, determined by the number of non-dominated solutions found so far. In μ FAME (and generally in μ GAs) the premature convergence problem acquires especial relevance, and generating and maintaining solutions diversity is of extreme importance to deal with it. This is especially significant in the multi-objective field, where the algorithm looks for a wide and diverse set of non-dominated solutions. In μ FAME, the diversity loss that high elitism usually produces is overcome by the decisions driven by the Fuzzy Inference System (FIS), that chooses the most appropriate operators to apply during the evolution to promote both diversity and accuracy of solutions. The algorithm arises as a suitable solution for problems with a computationally heavy fitness function, because it is able to quickly achieve good quality local optimal solutions, but it is also highly competitive in long runs. Experiments reveal a great performance of μ FAME according to several convergence related metrics, when comparing against seven state-of-the-art algorithms on a large set of benchmark problems, as well as on a real-world engineering one with an expensive fitness function.

This work presents a novel parallel branch and bound algorithm to efficiently solve to optimality a set of instances of the multi-objective flexible job shop scheduling problem for the first time, to the very best of our knowledge. It makes use of the well-known NSGA-II algorithm to initialize its upper bound. The algorithm is implemented for shared-memory architectures, and among its main features, it incorporates a grid representation of the solution space, and a concurrent priority queue to store and dispatch the pending sub-problems to be solved. We report the optimal Pareto front of thirteen well-known instances from the literature, which were unknown before. They will be very useful for the scientific community to provide more accuracy in the performance measurement of their algorithms. Indeed, we carefully analyze the performance of NSGA-II on these instances, comparing the results against the optimal ones computed in this work. Extensive computational experiments show that the proposed algorithm using 24 cores achieves a speedup of 15.64x with an efficiency of 65.20%.

We present in this work the first parallel implementation of Virtual Savant (VS), a novel optimization method that is able to quickly generate pseudo-optimal solutions to a given combinatorial problem, thanks to its parallel pattern recognition engine. The proposed parallel implementation does not require any information exchange between the threads during the run, they just get/send the required information before/after the execution. This design allows for a flexible algorithm that can perform efficiently on both shared- and distributed-memory systems. Our implementation uses both OpenMP for parallel architectures and MPI for distributed environments, which can efficiently make use of both kind of systems. The performance of VS is extensively analyzed on four different computing infrastructures, varying the number of threads used on each considered architecture. In addition, we propose a simulator to accurately predict the performance of VS on any parallel system. Experimental results show that VS is able to make an efficient use of the available computing resources, showing good scalability properties on all studied architectures.

A key issue when using distributed computing environments is finding a planning strategy to execute tasks in order to use the computational resources efficiently. This article presents the application of Virtual Savant to solve the heterogeneous computing scheduling problem, a widely-studied problem with several real-world applications. Virtual Savant is a novel method that uses machine learning techniques to automatically generate programs that can be executed in parallel to solve a given problem. Experimental analysis is performed on a set of problem instances generated following methodologies from the related literature. Results show that Virtual Savant is able to outperform MinMin, a well-known heuristic for the studied problem, by up to 15% while showing good scalability properties when increasing the number of computing resources and the dimension of the problem instances.

Over the last ten years, there has been a significant change in computing. After reaching the physical limits in integrated circuit technology, Moore's law (predicting an exponential growth of computing capacity over the years) has been maintained thanks to the replication of computing components (i.e., parallelization). Current computers now incorporate multiple processors, which are themselves multi-core, and come with technologies to increase the parallel capabilities of processors, like the Intel hyper-threading technology that implements two logical processors per core. This is the architecture present not only in today's servers or workstations, but also in portable and mobile devices as laptops, tablets, or mobile phones. It is accepted that this trend towards more and more parallel architectures will be maintained in the coming years.

Literature reveals that optimization algorithms are generally composed of a large number of parameters that highly influence on its performance. In the early stages of the definition of a new algorithm, it is crucial to know how the uncertainty in the input parameters affects the behavior of the algorithm, influencing on its final output, so that it is possible to set up the most efficient configuration.

MANETs are specially vulnerable against security attacks. For protecting them, security solutions are traditionally addressed by the so-called intrusion detection and response systems. Nevertheless, using reactive (response) solutions, once the attack is detected, rely in long attack mitigation times. In this work, we propose the use of relay node placement techniques as proactive security solutions. Preliminary experimentation demonstrates the suitability and feasibility of such approaches, not only in reducing the attack mitigation time but in replacing detection and response based traditional solutions.

Sensitivity analysis is a mathematical tool that distributes the uncertainty of the output of a model among its different input variables. We use in this work the Extended Fourier Amplitude Sensitivity Test to carefully analyze the impact of 54 LLVM code optimization operators on the execution time of nine benchmark software programs. Experiments presented involve performing over 16 million executions. The results show that the different LLVM transformations have a low direct effect on the execution time, but it becomes meaningful when considering the transformation in combination with the others (almost 60% average impact by all passes on all considered benchmarks). These results provide slight indications on the transformations to apply for optimizing the software, revealing the extreme difficulty of the problem.

This article presents and evaluates twenty-four novel bi-objective efficient heuristics for the simultaneous optimization of makespan and robustness in the context of the static robust tasks mapping problem for datacenters. The experimental analysis compares the proposed methods over realistic problem scenarios. We study their accuracy, as well as the regions of the search space they explore, by comparing versus state-of-the-art Pareto fronts, obtained with four different specialized versions of well-known multi-objective evolutionary algorithms.

Support vector machines are widely used for classification and regression tasks. However, sequential implementations for support vector machines are usually unable to deal with the increasing size of current real-world learning problems. In this context, Intel®Xeon PhiTM processors allow easily incorporating high performance computing strategies to improve execution times. This article proposes a parallel implementation of the popular LIBSVM library, specially adapted to the Intel®Xeon PhiTM architecture. The proposed implementation is evaluated using publicly available datasets corresponding to classification and regression tasks. Results show that the proposed parallel version computes the same results than the original LIBSVM while reducing the time needed for training by up to a factor of 4.81.

This chapter presents a new kind of cloud brokering model called virtual broker. The virtual broker owns and manages what we call a virtual cloud, composed by a set of reserved VMs from a number of public cloud providers. This new broker sublets its resources to its customers as on-demand VMs, at lower prices than those offered in the market. This model is feasible because of the large price difference between on-demand and reserved VMs in current pricing schemes. We propose a number of online and offline heuristics to efficiently manage the resources of the virtual broker in order to optimize its revenue, as well as the QoS level offered to the customers. Two realistic versions of the problem are proposed and analyzed. The results show the proposed brokering model is a profitable business model for both the virtual broker and the cloud customers, reducing cloud customer costs down to 80% when compared to traditional on-demand renting costs.

This article addresses a virtual machine (VM) allocation problem that appears in a novel business model for cloud computing. In this model, a cloud service broker owns a number of cloud reserved instances that outsources to its customers as cheap as on-demand VMs. The objective of the broker is to efficiently manage its reserved resources to maximize its revenue. We enhance the previous definition of the problem by considering more realistic parameters: geographical localization of resources and users, different types of applications, and data transfer costs. We propose a set of heuristics to solve the optimization problem of maximizing the cloud provider profit while offering appropriate Quality-of-Service to the users. The experimental analysis is performed over different scenarios using real data from cloud providers.

Computer-aided design (CAD) is a technological revolution, very powerful and with large applicability to problem solving. It is essential in many different disciplines ranging from architecture to education, medicine, physics, or gaming. In this work, we propose a novel CAD tool, called CADDi, to assist in the design of electric diagrams in the educational context. We are applying the theory of formal languages to create WDLang, an easy-to-use, highly expressive, unequivocal, and correct programming language for designing electric circuits. This programming language is the cornerstone of CADDi, which automatically generates the equivalent ladder diagram (explains the circuit operation) to the programmed circuit, offering additional features that allow analysis of its functionality in an interactive way. It also offers a graphical interface to directly design ladder diagrams, or to modify the automatically generated ones. The existing electrical CAD tools are either very simple, e.g., for creating good-looking diagrams with no functionality, or too complex, for professional systems design. CADDi is extremely useful for learning purposes. It assists users on how to generate ladder diagrams, and on understanding the behavior of electrical circuits. Additionally, it serves as an assessment tool for self-evaluation in the translation from wiring diagrams to ladder ones. In order to make CADDi highly accessible, it was implemented as a web page.

This paper presents a new parallel multi-objective cooperative coevolutionary variant of the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm. SMPSO adopts a strategy for limiting the velocity of the particles that prevents them from having erratic movements. This characteristic provides the algorithm with a high degree of reliability. The proposed approach, called CCSMPSO, is based on a new design and implementation of SMPSO in a cooperative coevolutionary (CC) framework. In such an architecture, the population is split into several subpopulations, which are in turn in charge of optimizing a subset of the global solution by using the original multi-objective algorithm. We compare our work with two different state-of-the-art multi-objective CC metaheuristics, namely CCNSGA-II and CCSPEA2, as well as the original SMPSO in order to demonstrate its effectiveness. Our experiments indicate that our proposed solution, CCSMPSO, offers significant computational speedups, a higher convergence speed and better and comparable results in terms of solution quality, when compared to the other two CC algorithms and SMPSO, respectively. Three different criteria are used for making the comparisons, namely the quality of the resulting approximation sets, average computational time and the convergence speed to the Pareto front.

The energy consumption of large data centers has been increasing for the last decades and currently is a major concern for economic and environmental reasons. Accurate scheduling of the data center operation and use of renewable energy sources present themselves as promising solutions for this problem. In this paper we study the problem of scheduling workflows of tasks in distributed heterogeneous data centers which are partially powered by renewable energy sources. This problem takes into account quality of service, infrastructure usage, and power consumption of machines and cooling devices. We propose a mathematical model for accurate scheduling solutions.

Evolutionary algorithms are metaheuristic algorithms that provide quasioptimal solutions in a reasonable time. They have been applied to many optimization problems in a high number of scientific areas. In this survey paper, we focus on the application of evolutionary algorithms to solve optimization problems related to a type of complex network likemobilemultihop ad hoc networks. Since its origin, mobile multihop ad hoc network has evolved causing new types of multihop networks to appear such as vehicular ad hoc networks and delay tolerant networks, leading to the solution of new issues and optimization problems. In this survey, we review the main work presented for each type of mobile multihop ad hoc network and we also present some innovative ideas and open challenges to guide further research in this topic.

This article presents a multiobjective approach for scheduling large workflows in distributed datacenters. We consider a realistic scheduling scenario of distributed cluster systems composed of multi-core computers, and a multi-objective formulation of the scheduling problem to minimize makespan, energy consumption and deadline violations. The studied schedulers follow a two-level schema: in the higher-level, we apply a multiobjective heuristic and a multiobjective metaheuristic, to distribute jobs between clusters; in the lower-level, specific backfilling-oriented scheduling methods are used for task scheduling locally within each cluster, considering precedence constraints. A new model for energy consumption in multi-core computers is applied. The experimental evaluation performed on a benchmark set of large workloads that model different realistic high performance computing applications demonstrates that the proposed multiobjective schedulers are able to improve both the makespan and energy consumption of the schedules when compared with a standard Optimistic Load Balancing Round Robin approach.

HighlightsHeterogeneous, energy-aware precedence constrained (DAG) scheduling problem.Three multi-objective algorithms schemas are adapted: MOCell, NSGAII and IBEA.A representation with corresponding mutations and grouping crossover operators.Experimentation on a diversified and large set of real and synthetic applications.MOCell schema is the most versatile and the best performing one. The ongoing increase of energy consumption by IT infrastructures forces data center managers to find innovative ways to improve energy efficiency. The latter is also a focal point for different branches of computer science due to its financial, ecological, political, and technical consequences. One of the answers is given by scheduling combined with dynamic voltage scaling technique to optimize the energy consumption. The way of reasoning is based on the link between current semiconductor technologies and energy state management of processors, where sacrificing the performance can save energy.This paper is devoted to investigate and solve the multi-objective precedence constrained application scheduling problem on a distributed computing system, and it has two main aims: the creation of general algorithms to solve the problem and the examination of the problem by means of the thorough analysis of the results returned by the algorithms.The first aim was achieved in two steps: adaptation of state-of-the-art multi-objective evolutionary algorithms by designing new operators and their validation in terms of performance and energy. The second aim was accomplished by performing an extensive number of algorithms executions on a large and diverse benchmark and the further analysis of performance among the proposed algorithms. Finally, the study proves the validity of the proposed method, points out the best-compared multi-objective algorithm schema, and the most important factors for the algorithms performance.

This paper studies a self-organized criticality model called sandpile for dynamically load-balancing tasks arriving in the form of Bag-of-Tasks in large-scale decentralized system. The sandpile is designed as a decentralized agent system characterizing a cellular automaton, which works in a critical state at the edge of chaos. Depending on the state of the cellular automaton, different responses may occur when a new task is assigned to a resource: it may change nothing or generate avalanches that reconfigure the state of the system. The abundance of such avalanches is in power-law relation with their sizes, a scale-invariant behavior that emerges without requiring tuning or control parameters. That means that large--catastrophic--avalanches are very rare but small ones occur very often. Such emergent pattern can be efficiently adapted for non-clairvoyant scheduling, where tasks are load balanced in computing resources trying to maximize the performance but without assuming any knowledge on the tasks features. The algorithm design is experimentally validated showing that the sandpile is able to find near-optimal schedules by reacting differently to different conditions of workloads and architectures.

International audience; Green Light Optimal Speed Advisory (GLOSA) systems provide drivers with speed advices that allow them to pass traffic lights during green interval. In this article we compare performance of single- and multi-segment GLOSA approaches. In a single-segment GLOSA traffic signals are considered independently, i.e. the system provides vehicles with the optimal speed for the segment ahead of the nearest traffic signals. In a multi-segment GLOSA several signals in a sequence on a vehicle's route are taken into account, that is, vehicles receive speed advices for a set of segments ahead of the vehicle. Traveling time and fuel efficiency are used as performance measures. We demonstrate that as long as traffic conditions allow drivers selecting a wide range of speed (e.g. during free-flow) a multi-segment GLOSA results in much better performance when compared with a single-segment approach.

International audience; In this paper, we simplify a Parallel Asynchronous Cellular Genetic Algorithm, by removing thread locks for shared memory access. This deliberate error aims to accelerate the algorithm, while preserving its search capability. Experiments with three benchmark problems show an acceleration, and even a slight improvement in search capability, with statistical significance.

International audience; This paper investigates the automatic parallelization of a heuristic for an NP-complete problem, with machine learning. The objective is to automatically design a new concurrent algorithm that finds solutions of comparable quality to the original heuristic. Our approach, called Savant, is inspired from the Savant syndrome. Its concurrency model is based on map-reduce. The approach is evaluated with the well-known Min-Min heuristic. Simulation results on two problem sizes are promising, the produced algorithm is able to find solutions of comparable quality.

International audience; The advantages of using structured populations in Evolutionary Algorithms (EAs) and other metaheuristicas are well known today, not only for parallelization purposes, but also for enhancing the performance of the algorithm with respect to the equivalent algorithm with panmictic population, even for sequential executions. The use of an appropriate population structure will allow us to create a higher efficiency and efficacy algorithm once structured, both numerically and in real time (e.g. parallelism). The articles composing this special issue represent excellent novel contributions in this direction. We collected six high-quality papers out of the 43 received ones. These finally selected papers deal with different aspects related to the topic of this special issue, such as theoretical studies, identification of current challenges in the field, novel population structure designs, and empirical validation on highly complex problems.

International audience; The development of large scale data center and cloud computing optimization models led to a wide range of complex issues like scaling, operation cost and energy efficiency. Different approaches were proposed to this end, including classical resource allocation heuristics, machine learning or stochastic optimization. No consensus exists but a trend towards using many-objective stochastic models became apparent over the past years. This work reviews in brief some of the more recent studies on cloud computing modeling and optimization, and points at notions on stability, convergence, definitions or results that could serve to analyze, respectively build accurate cloud computing models. A very brief discussion of simulation frameworks that include support for energy-aware components is also given.

International audience; Mobile ad hoc networks are infrastructure less communication networks that are spontaneously created by a number of mobile devices. Due to the highly fluctuating topology of such networks, finding the optimal configuration of communication protocols is a complex and crucial task. Additionally, different objectives must be usually considered. Small changes in the values of the parameters directly affects the performance of the protocol, promoting one objective while reducing another. Therefore, multi-objective optimisation is needed for fine tuning the protocol. In this work, we propose a novel parallel multi-objective local search that optimises an energy efficient broadcasting algorithm in terms of coverage, energy used, broadcasting time, and network resources. The proposed method looks for appropriate values for a set of 5 variables that markedly influence the behavior of the protocol to provide accurate tradeoff configurations in a reasonable short execution time. The new proposed algorithm is validated versus two efficient multi-objective evolutionary algorithms from the state of the art, offering comparable quality results in much shorter times.

International audience; Advantages of bringing small-world properties in mobile ad hoc networks (MANETs) in terms of quality of service has been studied and outlined in the past years. In this work, we focus on the specific class of vehicular ad hoc networks (VANETs) and propose to un-partition such networks and improve their small-world properties. To this end, a subset of nodes, called injection points, is chosen to provide backend connectivity and compose a fully-connected overlay network. The optimisation problem we consider is to find the minimal set of injection points to constitute the overlay that will optimise the small-world properties of the resulting network, i.e., (1) maximising the clustering coefficient (CC) so that it approaches the CC of a corresponding regular graph and (2) minimising the difference between the average path length (APL) of the considered graph and the APL of corresponding random graphs. In order to face this new multi-objective optimisation problem, the NSGAII algorithm was used on realistic instances in the city-centre of Luxembourg. The accurate tradeoff solutions found by NSGAII (assuming global knowledge of the network) will permit to better know and understand the problem. This will later ease the design of decentralised solutions to be used in real environments, as well as their future validation.

International audience; This article presents a new parallel hybrid evolutionary algorithm to solve the problem of virtual machines subletting in cloud systems. The problem deals with the efficient allocation of a set of virtual machine requests from customers into available pre-booked resources from a cloud broker, in order to maximize the broker profit. The proposed parallel algorithm uses a distributed subpopulations model, and a Simulated Annealing operator. The experimental evaluation analyzes the profit and make span results of the proposed methods over a set of problem instances that account for realistic workloads and scenarios using real data from cloud providers. A comparison with greedy heuristics indicates that the proposed method is able to compute solutions with up to 133.8% improvement in the profit values, while accounting for accurate make span results.