The Missing Link! A New Skeleton for Evolutionary Multi-agent Systems in Erlang

Evolutionary multi-agent systems (EMAS) play a critical role in many artificial intelligence applications that are in use today. In this paper, we present a new generic skeleton in Erlang for parallel EMAS computations. The skeleton enables us to capture a wide variety of concrete evolutionary computations that can exploit the same underlying parallel implementation. We demonstrate the use of our skeleton on two different evolutionary computing applications: (1) computing the minimum of the Rastrigin function; and (2) solving an urban traffic optimisation problem. We show that we can obtain very good speedups (up to 142.44 ×× the sequential performance) on a variety of different parallel hardware, while requiring very little parallelisation effort.Publisher PDFPeer reviewe

Proposed Algorithm for Scheduling in Computational Grid using Backfilling and Optimization Techniques

In recent years, the fast evolution in the industry of computer hardware such as the processors, has led the application developers to design advanced software's that require massive computational power. Thus, grid computing has emerged in order to handle the computational power demands requested by the applications. Quality of service (QoS) in grid is highly required in order to provide a high service level to the users of Grid. Several interactions events are involved in determining the QoS level in grid such as; allocating the resources for the jobs, monitoring the performance of the selected resources and the computing capability of the available resources. To allocate the suitable resources for the incoming jobs, a scheduling algorithm has to manage this process. In this paper, we provide a critical review the recent mechanisms in “grid computing” environment. In addition, we propose a new scheduling algorithm to minimize the delay for the end user, Gap Filling policy will be applied to improve the performance of the priority algorithm. Then, an optimization algorithm will perform in order to further enhance the initial result for that obtained from backfilling mechanism. The main aim of the proposed scheduling mechanism is to improve the QoS for the end user in a real grid computing environment

Proposed algorithm for scheduling in computational grid using backfilling and optimization techniques

In recent years, the fast evolution in the industry of computer hardware such as the processors, has led the application developers to design advanced software's that require massive computational power.Thus, grid computing has emerged in order to handle the computational power demands requested by the applications.Quality of service (QoS) in grid is highly required in order to provide a high service level to the users of Grid.Several interactions events are involved in determining the QoS level in grid such as; allocating the resources for the jobs, monitoring the performance of the selected resources and the computing capability of the available resources. To allocate the suitable resources for the incoming jobs, a scheduling algorithm has to manage this process.In this paper, we provide a critical review the recent mechanisms in “grid computing” environment.In addition, we propose a new scheduling algorithm to minimize the delay for the end user, Gap Filling policy will be applied to improve the performance of the priority algorithm.Then, an optimization algorithm will perform in order to further enhance the initial result for that obtained from backfilling mechanism.The main aim of the proposed scheduling mechanism is to improve the QoS for the end user in a real grid computing environment

Agent-Based Model of Fab Lab Communities

RÉSUMÉ : Étant donné que les concepts d'innovation socio-technologique ont accéléré à la direction de la personnalisation massive, la fabrication par des laboratoires "Fab Lab" sera le prochain domaine intéressant à trouver son chemin vers la personnalisation dans un contexte collaboratif. Elle a été reconnue comme la prochaine révolution industrielle (Morel & Le Roux, 2016; Troxler, 2013), puisqu'elle peut soutenir de nouvelles innovations technologiques collaboratives en autorisant des individus à utiliser leurs ressources locales et de trouver leurs solutions économiques pratiques (Gershenfeld, 2006; Morel, Dupont, & Lhoste, 2015). Ce nouveau concept de collaboration communautaire peut être utilisé dans différents segments de service fournissant, c'est-à-dire des fins éducatives, des solutions de production, des pratiques personnelles, etc. Pourtant, il n'y a pas assez d'études pratiques pour aider le processus de choisir les stratégies les plus appropriées et des méthodes pour développer des interactions personnelles par les types différents de communautés Fab Lab. En conséquence, une simulation à base d'agent semble être un outil utile pour soutenir la conception des Fab Labs comme le futur modèle répandu pour les processus d'innovation, de fabrication ou d'apprentissage des compétences. Cette étude propose un modèle à base d'agent qui a été simulé en utilisant la plateforme AnyLogic et a été développé par un codage Java supplémentaire. En tenant compte de divers facteurs, il a été évalué par certaines techniques de vérification et de validation. De plus, deux séries d'expériences ont été menées pour soutenir la validité de ce modèle puisqu'il n'y a pas de données empiriques ni de variantes historiques disponibles pour comparer et vérifier les résultats de cette simulation avec une communauté Fab Lab réelle. En plus, d'autres expériences ont été menées afin d'étudier l'impact du seuil de déclenchement et l'intensité des programmes de motivation sur les interactions des membres de la communauté. Les résultats ont découvert des influences indéniables des programmes de motivation avec différentes configurations sur des communautés Fab Lab en termes de durée de la vie active, niveau de fait d'être actif, la compétence/ la connaissance transférée. Néanmoins, l'application des résultats dans certaines situations réelles peut révéler les contraintes cachées réelles pour améliorer ce modèle.----------ABSTRACT : Considering that socio-technological innovation concepts have been accelerating in the direction of mass customization, fabrication through labs “Fab Lab” is going to be the next interesting domain to find its way toward customization in a collaborative context. It has been recognized as the next industrial revolution (Morel & Le Roux, 2016; Troxler, 2013) since it can support new collaborative technological innovations by empowering individuals to use their local resources and to find their practical economic solutions (Gershenfeld, 2006; Morel et al., 2015). This new concept of community-based collaboration can be used in different service providing segments, i.e. educational purposes, production solutions, personal practices, etc. Yet, there are not enough practical studies to assist the process of choosing the most appropriate strategies and methods to develop personal interactions through different types of Fab Lab communities. Accordingly, an agent-based simulation seems to be a useful tool to support the design of Fab Labs as the future widespread model for innovation, fabrication, or skill learning processes. This study proposes an agent-based model that was simulated using the AnyLogic platform and was developed by supplementary Java coding. In consideration of diverse factors, it was evaluated by some verification and validation techniques. Moreover, two series of experiments were carried out to support the validity of this model since there is neither related empirical data nor historical variants available to compare and check the results of this simulation with a real Fab Lab community. Besides, other experiments were conducted in order to study the impact of the triggering threshold and the intensity of motivation programs on interactions of the community members. The results uncovered undeniable influences of motivation programs with different setups on Fab Lab communities in terms of active lifespan, level of activeness, transferred skill/ knowledge. Nevertheless, applying the results in some real situations can reveal the actual concealed constrains to improve this model

Transactions on Computational Collective Intelligence X

XII, 207 p. 64 illus.online resource