Solving Stable Matching Problems via Cooperative Parallel Local Search

International audienceStable matching problems and its variants have several practical applications, like the Hospital/Residents problem, stable roommates problem or bipartite market sharing. An important generalization problem is the SMTI which allows for incompleteness and ties in the user's preference lists. Finding a maximal size stable matching for SMTI is compu-tationally difficult. We developed a Local Search method to solve SMTI using the Adaptive Search algorithm and present experimental evidence that this approach is much more efficient than state-of-the-art exact and approximate methods (in terms of both computational effort required and quality of solution). We also tried a parallel version of our algorithm. For this we reused the Cooperative Parallel Local Search framework (CPLS) we designed. CPLS is a highly parametric framework for the execution in parallel of local search solvers allowing them to cooperate though communication. The cooperative parallel version of our local search algorithm improves performance so much that very large and hard instances can be solved quickly

Solving the Quadratic Assignment Problem with Cooperative Parallel Extremal Optimization

International audienceSeveral real-life applications can be stated in terms of the Quadratic Assignment Problem. Finding an optimal assignment is com-putationally very difficult, for many useful instances. We address this problem using a local search technique, based on Extremal Optimization and present experimental evidence that this approach is competitive. Moreover, cooperative parallel versions of our solver improve performance so much that large and hard instances can be solved quickly

Hybridization as Cooperative Parallelism for the Quadratic Assignment Problem

International audienceThe Quadratic Assignment Problem is at the core of several real-life applications. Finding an optimal assignment is computationally very difficult, for many useful instances. The best results are obtained with hybrid heuristics, which result in complex solvers. We propose an alternate solution where hybridization is obtain by means of parallelism and cooperation between simple single-heuristic solvers. We present experimental evidence that this approach is very efficient and can effectively solve a wide variety of hard problems, often surpassing state-of-the-art systems

Towards a Parallel Hierarchical Adaptive Solver Tool

International audienceConstraint satisfaction and combinatorial optimization problems , even when modeled with efficient metaheurisics such as local search remain computationally very intensive. Solvers stand to benefit significantly from execution on parallel systems, which are increasingly available. The architectural diversity and complexity of the latter means that these systems pose ever greater challenges in order to be effectively used, both from the point of view of the modeling effort and from that of the degree of coverage of the available computing resources. In this article we discuss impositions and design issues for a framework to make efficient use of various parallel architectures

IoT-based air quality monitoring systems for smart cities: A systematic mapping study

The increased level of air pollution in big cities has become a major concern for several organizations and authorities because of the risk it represents to human health. In this context, the technology has become a very useful tool in the contamination monitoring and the possible mitigation of its impact. Particularly, there are different proposals using the internet of things (IoT) paradigm that use interconnected sensors in order to measure different pollutants. In this paper, we develop a systematic mapping study defined by a five-step methodology to identify and analyze the research status in terms of IoT-based air pollution monitoring systems for smart cities. The study includes 55 proposals, some of which have been implemented in a real environment. We analyze and compare these proposals in terms of different parameters defined in the mapping and highlight some challenges for air quality monitoring systems implementation into the smart city context

Utilisation de méta-heuristiques coopératives parallèles pour la résolution de problèmes d'optimisation combinatoire difficiles

Les Problèmes d’Optimisation Combinatoire (COP) sont largement utilisés pour modéliser et résoudre un grand nombre de problèmes industriels. La résolution de ces problèmes pose un véritable défi en raison de leur inhérente difficulté, la plupart étant NP-difficiles. En effet, les COP sont difficiles à résoudre par des méthodes exactes car la taille de l’espace de recherche à explorer croît de manière exponentielle par rapport à la taille du problème. Les méta-heuristiques sont souvent les méthodes les plus efficaces pour résoudre les problèmes les plus difficiles. Malheureusement, bien des problèmes réels restent hors de portée des meilleures méta-heuristiques. Le parallélisme permet d’améliorer les performances des méta-heuristiques. L’idée de base est d’avoir plusieurs instances d’une méta-heuristique explorant de manière simultanée l’espace de recherche pour accélérer la recherche de solution. Les meilleures techniques font communiquer ces instances pour augmenter la probabilité de trouver une solution. Cependant, la conception d’une méthode parallèle coopérative n’est pas une tâche aisée, et beaucoup de choix cruciaux concernant la communication doivent être résolus. Malheureusement, nous savons qu’il n’existe pas d’unique configuration permettant de résoudre efficacement tous les problèmes. Ceci explique que l’on trouve aujourd’hui des systèmes coopératifs efficaces mais conçus pour un problème spécifique ou bien des systèmes plus génériques mais dont les performances sont en général limitées. Dans cette thèse nous proposons un cadre général pour les méta-heuristiques parallèles coopératives (CPMH). Ce cadre prévoit plusieurs paramètres permettant de contrôler la coopération. CPMH organise les instances de méta-heuristiques en équipes ; chaque équipe vise à intensifier la recherche dans une région particulière de l’espace de recherche. Cela se fait grâce à des communications intra-équipes. Des communications inter-équipes permettent quant a` elles d’assurer la diversification de la recherche. CPMH offre à l’utilisateur la possibilité d’ajuster le compromis entre intensification et diversification. De plus, ce cadre supporte différentes méta-heuristiques et permet aussi l’hybridation de méta-heuristiques. Nous proposons également X10CPMH, une implémentation de CPMH, écrite en langage parallèle X10. Pour valider notre approche, nous abordons deux COP du monde industriel : des variantes difficiles du Problème de Stable Matching (SMP) et le Problème d’Affectation Quadratique (QAP). Nous proposons plusieurs méta-heuristiques originales en version séquentielle et parallèle, y compris un nouvelle méthode basée sur l’optimisation extrémale ainsi qu’un nouvel algorithme hybride en parallèle coopératif pour QAP. Ces algorithmes sont implémentés grâce à X10CPMH. L’évaluation expérimentale montre que les versions avec parallélisme coopératif offrent un très bon passage à l’échelle tout en fournissant des solutions de haute qualité. Sur les variantes difficiles de SMP, notre méthode coopérative offre des facteurs d’accélération super-linéaires. En ce qui concerne QAP, notre méthode hybride en parallèle coopératif fonctionne très bien sur les cas les plus difficiles et permet d’améliorer les meilleures solutions connues de plusieurs instances.Combinatorial Optimization Problems (COP) are widely used to model and solve real-life problems in many different application domains. These problems represent a real challenge for the research community due to their inherent difficulty, as many of them are NP-hard. COPs are difficult to solve with exact methods due to the exponential growth of the problem’s search space with respect to the size of the problem. Metaheuristics are often the most efficient methods to make the hardest problems tractable. However, some hard and large real-life problems are still out of the scope of even the best metaheuristic algorithms. Parallelism is a straightforward way to improve metaheuristics performance. The basic idea is to perform concurrent explorations of the search space in order to speed up the search process. Currently, the most advanced techniques implement some communication mechanism to exchange information between metaheuristic instances in order to try and increase the probability to find a solution. However, designing an efficient cooperative parallel method is a very complex task, and many issues about communication must be solved. Furthermore, it is known that no unique cooperative configuration may efficiently tackle all problems. This is why there are currently efficient cooperative solutions dedicated to some specific problems or more general cooperative methods but with limited performances in practice. In this thesis we propose a general framework for Cooperative Parallel Metaheuristics (CPMH). This framework includes several parameters to control the cooperation. CPMH organizes the explorers into teams; each team aims at intensifying the search in a particular region of the search space and uses intra-team communication. In addition, inter-team communication is used to ensure search diversification. CPMH allows the user to tune the trade-off between intensification and diversification. However, our framework supports different metaheuristics and metaheuristics hybridization. We also provide X10CPMH, an implementation of our CPMH framework developed in the X10 parallel language. To assess the soundness of our approach we tackle two hard real-life COP: hard variants of the Stable Matching Problem (SMP) and the Quadratic Assignment Problem (QAP). For all problems we propose new sequential and parallel metaheuristics, including a new Extremal Optimization-based method and a new hybrid cooperative parallel algorithm for QAP. All algorithms are implemented thanks to X10CPMH. A complete experimental evaluation shows that the cooperative parallel versions of our methods scale very well, providing high-quality solutions within a limited timeout. On hard and large variants of SMP, our cooperative parallel method reaches super-linear speedups. Regarding QAP, the cooperative parallel hybrid algorithm performs very well on the hardest instances, and improves the best known solutions of several instances

Experimenting with X10 for Parallel Constraint-Based Local Search

Abstract. In this study, we have investigated the adequacy of the PGAS parallel language X10 to implement a Constraint-Based Local Search solver. We decided to code in this language to benefit from the ease of use and architectural independence from parallel resources which it offers. We present the implementation strategy, in search of different sources of parallelism in the context of an implementation of the Adaptive Search algorithm. We extensively discuss the algorithm and its implementation. The performance evaluation on a representative set of benchmarks shows close to linear speed-ups, in all the problems treated.

Flexible cooperation in parallel local search (extended abstract)

International audienceConstraint-Based Local Search (CBLS) consist in using Local Search methods [4] for solving Constraint Satisfac- tion Problems (CSP). In order to further improve the per- formance of Local Search, one possible option is to take advantage of the increasing availability of parallel compu- tational resources. Parallel implementation of local search meta-heuristics has been studied since the early 90’s, when multiprocessor machines started to become widely available, see [6]. One usually distinguishes between single-walk and multiple-walk methods: Single-walk methods consist in us- ing parallelism inside a single search process, e.g. for paral- lelizing the exploration of the neighborhood, while multiple- walk methods (also called multi-start methods) consist in de- veloping concurrent explorations of the search space, either independently (IW) or cooperatively (CW) with some com- munication between concurrent processes. Although good results can be achieved just with IW [1], a more sophisticated paradigm featuring cooperation between independent walks should bring better performance. We thus propose a gen- eral framework for cooperative search, which defines a flexi- ble and parametric strategy based on the cooperative multi- walk (CW) scheme. The framework is oriented towards dis- tributed architectures based on clusters of nodes, with the notion of “teams” running on nodes which group several in- dividual search engines (e.g. multicore nodes). The idea is that teams are distributed and thus have limited inter-node communication. This framework allows the programmer to define aspects such as the degree of intensification and di- versification present in the parallel search process. A good trade-off is essential to reach high performance. A prelimi- nary implementation of the general CW framework has been done in the X10 programming language [5], and performance evaluation over a set of well-known benchmark CSPs shows that CW consistently outperforms IW

On Integrating Population-Based Metaheuristics with Cooperative Parallelism

International audienceMany real-life applications can be formulated as Combinatorial Optimization Problems, the solution of which is often challenging due to their intrinsic difficulty. At present, the most effective methods to address the hardest problems entail the hybridization of metaheuristics and cooperative parallelism. Recently, a framework called CPLS has been proposed, which eases the cooperative parallelization of local search solvers. Being able to run different heuristics in parallel, CPLS has opened a new way to hybridize metaheuristics, thanks to its cooperative parallelism mechanism. However, CPLS is mainly designed for local search methods. In this paper we seek to overcome the current CPLS limitation, extending it to enable population-based metaheuristics in the hybridization process. We discuss an initial prototype implementation for Quadratic Assignment Problem combining a Genetic Algorithm with two local search procedures. Our experiments on hard instances of QAP show that this hybrid solver performs competitively w.r.t. dedicated QAP parallel solvers