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2009Ines Thabet, ,
Towards an Adaptive Grid Scheduling: Architecture and Protocols Specification
KES-AMSTA 2009: 599-608, 2009
Abstract
Grid is known to be a heterogeneous, distributed and dynamic environment. In order to take fully advantages from grid power, Grid scheduling must take into consideration the environment’s constraints and be adaptive. In this work, Grid architecture is fully rethought in terms of agents in order to implement a cooperative and adaptive scheduling. At a macro level, our architecture enables flexible cooperation among its components using high level interaction protocols. At the micro level, agents in charge of scheduling perform an adaptive behaviour since they are able to perceive their environment and its disturbances, to reason and to deliberate about the actions to undertake in order to adapt. This is made possible by the use of Belief-Desire-Intention mechanisms. For that purpose, we propose a conceptual model useful for the perception function. Also, a typology of adaptive rules useful for the deliberation step is given. Component’s behaviour are specified and simulated with Petri-Nets
Saoussen Bel Haj Kacem, ,On Some Properties of Generalized Symbolic Modifiers and Their Role in Symbolic Approximate Reasoning
In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg., 2009
Abstract
Linguistic modifiers, defined by Zadeh in fuzzy logic context, are operators that transform a linguistic term to another linguistic term. Akdag and al. extend linguistic modifiers to symbolic multi-valued logic context, and called them Generalized Symbolic Modifiers. In this paper we propose a study which allows deepening the use of Generalized Symbolic Modifiers in soft computing applications. We focus on symbolic modifiers composition, and we give new properties. Then, we study modifiers order relation, based on a lattice that orders all the defined modifiers according to their parameters. Finally, we illustrate the utilities of our propositions, particularly in approximate reasoning based on linguistic modifiers.
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2008Meriem Ennigrou,
New local diversification techniques for the Flexible Job Shop problem with a Multi-agent approach
Journal of Autonomous Agents and Multi-Agent Systems JAAMAS, 2008
Abstract
The Flexible Job Shop problem is among the hardest scheduling problems. It is a generalization of the classical Job Shop problem in that each operation can be processed by a set of resources and has a processing time depending on the resource used. The objective is to assign and to sequence the operations on the resources so that they are processed in the smallest time. In our previous work, we have proposed two Multi-Agent approaches based on the Tabu Search (TS) meta-heuristic. Depending on the location of the optimisation core in the system, we have distinguished between the global optimisation approach where the TS has a global view on the system and the local optimisation approach (FJS MATSLO) where the optimisation is distributed among a collection of agents, each of them has its own local view. In this paper, firstly, we propose new diversification techniques for the second approach in order to get better results and secondly, we propose a new promising approach combining the two latter ones. Experimental results are also presented in this paper in order to evaluate these new techniques.
, Lamjed Ben Said,Genetic optimization of the multi-location transshipment problem with limited storage capacity
ECAI 2008 (pp. 563-567). IOS Press., 2008
Abstract
Lateral Transshipments afford a valuable mechanism for compensating unmet demands only with on-hand inventory. In this paper we investigate the case where locations have a limited storage capacity. The problem is to determine how much to replenish each period to minimize the expected global cost while satisfying storage capacity constraints. We propose a Real-Coded Genetic Algorithm (RCGA) with a new crossover operator to approximate the optimal solution. We analyze the impact of different structures of storage capacities on the system behaviour. We find that Transshipments are able to correct the discrepancies between the constrained and the unconstrained locations while ensuring low costs and system-wide inventories. Our genetic algorithm proves its ability to solve instances of the problem with high accuracy.
, Lamjed Ben Said,Evolutionary multiobjective optimization of the multi-location transshipment problem
Operational Research International Journal 8, 167–183, 2008
Abstract
We consider a multi-location inventory system where inventory choices at each location are centrally coordinated. Lateral transshipments are allowed as recourse actions within the same echelon in the inventory system to reduce costs and improve service level. However, this transshipment process usually causes undesirable lead times. In this paper, we propose a multiobjective model of the multi-location transshipment problem which addresses optimizing three conflicting objectives: (1) minimizing the aggregate expected cost, (2) maximizing the expected fill rate, and (3) minimizing the expected transshipment lead times. We apply an evolutionary multiobjective optimization approach using the strength Pareto evolutionary algorithm (SPEA2), to approximate the optimal Pareto front. Simulation with a wide choice of model parameters shows the different trades-off between the conflicting objectives.
Ines Thabet, ,Vers une architecture de type agent BDI pour un ordonnanceur de grille adaptatif
CAL 2008: 19-33, 2008
Abstract
Un Ordonnanceur de Grille (OG) a pour objectif d’optimiser l’utilisation des ressources mises à sa disposition. Le plus souvent, ce composant manque de flexibilité dans sa conception et a des difficultés à s’adapter aux diverses perturbations (fluctuation de la qualité de service, arrivée ou départ de nouvelles ressources, arrivée de travaux prioritaires, etc). L’objectif de cet article est de proposer une architecture orientée agents de type BDI (Belief Desire Intention) pour permettre à l’OG de s’adapter à ces perturbations et de travailler en coopération avec ses accointances. L’OG proposé fonctionne en boucle selon un schéma perception-délibération-action. La perception de l’environnement et de ses perturbations est rendue possible ici à l’aide d’un modèle conceptuel qui décrit à la fois la structure de la Grille et son fonctionnement. La délibération décide des actions à entreprendre (migration d’application, changement de plan d’ordonnancement, changement de politique d’attribution des tâches,..) pour s’adapter aux perturbations sur la base de règles qui proposent différents types d’adaptation selon les événements détectés. L’action correspond à la mise en oeuvre des décisions prises lors de la délibération.
Saoussen Bel Haj Kacem, ,Generalized Modus Ponens Based on Linguistic Modifiers in a Symbolic Multi-Valued Framework
38th International Symposium on Multiple Valued Logic (ismvl 2008). IEEE, 2008., 2008
Abstract
Approximate reasoning is based on a generalization of Modus Ponens, known as Generalized Modus Ponens (GMP). Its principle is that from an observation different but approximately equal to the rule premise, we can deduce a fact approximately equal to the rule conclusion. However, the deduced fact is not obtained with an arbitrary way. A set of axioms are fixed to have coherent and logic results. In this paper, we propose a new rule of GMP in the multi-valued logic framework. Our aim is to provide inference results which checks the axiomatic of approximate reasoning. We propose to use linguistic modifiers in GMP and we explain how it allows having a gradual reasoning.
Saoussen Bel Haj Kacem, ,Approximate Reasoning in a Symbolic Multi-valued Framework
In : 38th International Symposium on Multiple Valued Logic (ismvl 2008). IEEE, 2008. p. 150-155., 2008
Abstract
We focus in this paper on approximate reasoning in a symbolic framework, and more precisely in multi-valued logic. Approximate reasoning is based on a generalization of Modus Ponens, known as Generalized Modus Ponens (GMP). Its principle is that from an observation different but approximately equal to the rule premise, we can deduce a fact approximately equal to the rule conclusion. We propose a generalization of the approximate reasoning axiomatic introduced by Fukami, and we show the weakness of GMP approaches in the multi-valued context towards this axiomatic. Moreover, we propose two rules of symbolic GMP that check the axiomatic. One is based on the implication operator and the second on linguistic modifiers.
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2007Wassim Ayadi,
A Binary Decision Diagram to discover low threshold support frequent itemsets.
18th International Workshop on Database and Expert Systems Applications (DEXA 2007) Pages 509-513, 2007
Abstract
Discovering association rules that identify relationships among sets of items is an important problem in data mining. Finding frequent itemsets is computationally the most expensive step in association rule discovery and therefore it has grasped significant research focus [1]. Discovery of frequently occurring subsets of items, called itemsets, is the core of many data mining methods. Most of the previous studies adopt Apriori- like algorithms, whom iteratively generate candidate itemsets and check their occurrence frequencies in the database. These approaches suffer from serious costs of repeated passes over the analyzed database. In this paper, we propose a new BDD-based (Binary Decision Diagram) data structure called TreeSupBDD. The TREESUPBDD extends the idea claimed by the authors of FP-TREE [9] and ITL-Tree [5] structures, aiming to improve storage compression and to allow frequent pattern mining without an « explicit » candidate itemset generation step. To address this problem, we propose a novel method, called TreeSupBDD- MlNE, for reducing database activity of frequent itemset discovery algorithms. The idea of TREESUPBDD-MlNE consists in using a Binary Decision Diagram and a tree for representing both database and frequent itemsets. The proposed method requires one scan over the source database : to create the associated tree and BDD and check discovered itemset supports. The originality of our work stands on the fact that the proposed algorithm extracts the frequent itemsets directly from the TreeSupBDD. Carried out experiments showed very encouraged results. Its performance improvements have been shown in a series of our experiments. We extend the binary decision diagram structure to store transaction groups and propose a new method to discover frequents itemsets. To study the trade-offs in the new representation of transactions in binary decision diagram, we compare the performance of our algorithm with the fastest Apriori [2] implementation algorithm and the latest extension of FP-Growth [15]. We have tested all the algorithms using different benchmark datasets. The performance study shows that the new algorithm significantly reduces the processing time for mining frequent itemsets from dense datasets that contain relatively long patterns and for low threshold. We discuss the performance results in detail and also the strengths and limitations of our algorithm.
Wassim Ayadi,A Novel Parallel Boolean Approach for Discovering Frequent Itemsets
Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007) Pages 297-302, IEEE, 2007
Abstract
ships among sets of items is an important problem in data mining. Finding frequent itemsets is computationally the most expensive step in a association rules discovery algorithms. Therefore, it has grasped significant research focus. Most of the previous studies adopt Apriori-like algorithms, whom iteratively generate candidate itemsets and check their frequencies in the database. These approaches suffer from serious costs of repeated passes over the database. To address this problem, we propose a new parallel method, called PARALLELTREESUPBDD-MINE, for reducing cost time to find frequent itemset discovery algorithms. The idea of PRALLELTREESUPBDD-MINE consists in using a Binary De- cision Diagram (BDD) and a prefix tree for representing both database and frequent itemsets. The proposed method requires only one scan over the source database to create the associated tree and BDD and to check discovered itemset supports. The originality of our work stands on the fact that the proposed algorithm extracts in a parallel manner the frequent itemsets directly from the TREESUPBDD. We have tested our algorithm using different benchmark datasets and we have obtained good results. Keywords: Data mining, Association rules, Frequent itemsets, Binary decision diagram, Parallel data mining.
