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1 Task Allocation Oriented Users Decisions in Computational Grid<br/>Joanna Kolodziej, Fatos Xhafa<br/>1.1 Introduction<br/>1.2 Users' Layers in the Und Arcnitecture<br/>1.3 Grid Users Relations in Grid Scheduling<br/>1.3.1 Hierarchic Grid Infrastructure<br/>1.3.2 Users' Requirements, Relations and Strategies in Job<br/>Scheduling<br/>1.4 Game-Theoretic Models for Scheduling and Resource<br/>Management<br/>1.5 Solving the Grid Users' Games<br/>1.5.1 GA-Based Hybrid Approach<br/>1.6 A Case Study: Non-cooperative Asymmetric Stackelberg Game<br/>of the Grid Users in Independent Batch Scheduling<br/>1.6.1 Players' Cost Functions .<br/>1.6.2 Experiments Setting<br/>1.6.3 Computational Results<br/>1.7 Other Approaches<br/>1.7.1 Computational Economy<br/>1.7.2 Neural Networks and Markov Decision Processes ,<br/>1.8 Conclusions and Future Work<br/>2 Efficient Hierarchical Task Scheduling on GRIDS Accounting for<br/>Computation and Communications<br/>Johnatan E. Pecero, Frederic Pinel, Bemabe Dorronsoro,<br/>Gr^goire Danoy, Pascal Bouvry, Albert Y. Zomaya<br/>2.1 Introduction<br/>2.2 Models<br/>2.2.1 System Model<br/>2.2.2 Application Model<br/>2.2.3 Scheduling Model<br/>2.3 Resource Management System and Grid Scheduling<br/>2.3.1 Resource Management System<br/>2.3.2 Workflow Scheduling on the Grid: A Brief<br/>Taxonomy<br/>2.4 Proposed Approach: The Hierarchical Scheduler with<br/>Cooperative Local Schedulers<br/>2.4.1 Recursive Convex Clustering Algorithm<br/>2.4.2 DAG Partitioning Problem<br/>2.4.3 Local Scheduler<br/>2.5 Results<br/>2.6 Conclusion<br/>Multi-objective Cooperative Coevolutionary Evolutionary<br/>Algorithms for Continuous and Combinatorial Optimization<br/>Bemabe Dorronsoro, Grdgoire Danoy, Pascal Bouvry,<br/>Antonio J. Nebro<br/>3.1 Introduction<br/>3.2 Related Work<br/>3.3 Base Algorithms<br/>3.4 The Proposed Cooperative Coevolutionary Evolutionary<br/>Algorithms<br/>3.4.1 Cooperative Coevolutionary Evolutionary<br/>Algorithms<br/>3.4.2 Multi-objective Cooperative Coevolutionary<br/>Evolutionary Algorithms<br/>3.4.3 Parallel Multi-objective Cooperative Coevolutionary<br/>Evolutionary Algorithms<br/>3.5 Problems<br/>3.5.1 Continuous Problems<br/>3.5;2 Real-World Combinatorial Problem<br/>3.6 Experiments<br/>3.6.1 Configuration of Algorithms<br/>3.6.2 Methodology for the Comparisons .<br/>3.6.3 Results<br/>3.7 Conclusion and Future Work<br/>Parallel Evolutionary Algorithms for Energy Aware Scheduling<br/>Yacine Kessaci, Mohand Mezmaz, Nouredine Melab,<br/>El-Ghazali Taibi, Daniel Tuyttens<br/>4.1 Introduction<br/>4.2 Energy Aware Approaches<br/>4.3 Optimization Approaches<br/>4.3.1 Aggregation Approach<br/>4.3.2 Lexicographic Approach<br/>4.3.3 Pareto Approach<br/>4.4 System-Level Approaches<br/>4.4.1 Hardware-Level Approaches<br/>4.4.2 Software-Level Approaches<br/>4.5 Approaches According to Targeted Execution System<br/>4.5.1 Embedded Systems<br/>4.5.2 Computing Systems<br/>4.6 Problem Modeling<br/>4.6.1 System Model<br/>4.6.2 Application Model .<br/>4.6.3 Energy Model<br/>4.6.4 Scheduling Model<br/>4.7 A Case Study: A Parallel Evolutionary Algorithm<br/>4.7.1 Hybrid Approach<br/>4.7.2 Insular Approach<br/>4.7.3 Multi-start Approach<br/>4.8 Experiments and Results<br/>4.8.1 Experimental Settings<br/>4.8.2 Hybrid Approach<br/>4.8.3 Insular Approach<br/>4.8.4 Multi-start Approach<br/>4.9 Conclusions<br/>Biologically-Inspired Methods and Game Theory in<br/>Multi-criterion Decision Processes<br/>Pawel Jarosz, Tadeusz Burczynski<br/>5.1 Introduction<br/>5.2 Multi-criteria Decision Making and Multiobjective<br/>Optimization<br/>5.2.1 No-Preference Methods<br/>5.2.2 Posteriori Methods<br/>5.2.3 Priori Methods<br/>5.2.4 Interactive Methods<br/>5.2.5 Multiobjective Optimization<br/>5.3 Methods for Multiobjective Optimization<br/>5.3.1 Evolutionary Algorithms<br/>5.3.2 Artificial Immune Systems<br/>5.3.3 Game Theory<br/>5.3.4 Hybrid Evolutionary-uame Algorithm<br/>5.3.5 Immune Game Theory Multiobjective Algorithm<br/>IMGAMO<br/>5.4 Numerical Tests<br/>5.4.1 The ZDT2 Problem<br/>5.4.2 The ZDT3 Problem<br/>5.4.3 The ZDT6 Problem<br/>5.5 Concluding Remarks<br/>Advanced Planning in Vertically Integrated Supply Chains<br/>Maksud Ibrahimov, Arvind Mohais, Sven Schellenberg,<br/>Zbigniew Michalewicz<br/>6.1 Introduction<br/>6.2 Literature Review<br/>6.2.1 Supply Chain Management<br/>6.2.2 Time-Vaiying Constraints<br/>6.2.3 Computational Intelligence<br/>6.3 Wine Supply Chain<br/>6.3.1 Maturity Models<br/>6.3.2 Vintage Intake Planning<br/>6.3.3 Crushing<br/>6.3.4 Tank Farm<br/>6.3.5 Bottling<br/>6.3.6 Environmental Factors<br/>6.3.7 Summary<br/>6.4 Advanced Planning in Mining<br/>6.4.1 Problem Statement<br/>6.4.2 Constraints and Business Rules<br/>6.4.3 Functionality<br/>6.5 Conclusion and Future Works<br/>7 Efficient Data Sharing over Large-Scale Distributed<br/>Communities<br/>Juan Li, Samee Ullah Khan, Qingrui Li, Nasir Ghani,<br/>Nasro Min-Allah, Pascal Bouvry, Weiyi Zhang<br/>7.1 Introduction<br/>7.2 Related Work<br/>7.3 System Overview<br/>7.3.1 Problem Description<br/>7.3.2 A Multilayered Semantic Sharing Scheme<br/>7.3.3 From Schema to Ontology<br/>7.3.4 Semantic Similarity<br/>7.4 Semantics-Based Self-clustering .<br/>7.4.1 Joining the Right Semantic Cluster<br/>7.4.2 Dynamic Self-adjusting<br/>7.5 Query Evaluation<br/>7.5.1 Problems of Query Evaluation<br/>7.5.2 Semantics-Based Forwarding<br/>7.5.3 Containment-Based Caching.<br/>7.6 Experiment<br/>7.7 Conclusion<br/>Hierarchical Multi-Agent System for Heterogeneous Data<br/>Integration<br/>Aleksander Byrski, Marek Kisiel-Dorohinicki, Jacek Dajda,<br/>Grzegorz Dobrowolski, Edward Nawarecki<br/>8.1 Introduction<br/>8.2 AgE - Agent-Based Computation Framework<br/>8.3 Panorama of Systems for Integration of Heterogeneous<br/>Information<br/>8.4 Basic Model of Data Transformation<br/>8.5 Hierarchical Data Integration and Processing<br/>8.5.1 System Environment and User Interaction<br/>8.5.2 Agent-Based Data Integration Workflow Model<br/>8.5.3 Multi-Agent System Structure<br/>8.5.4 Tasks, Objects and Data Types<br/>8.5.5 Tree of Agents .<br/>8.5.6 Roles of Agents<br/>8.5.7 Actions of Agents<br/>8.5.8 Resources of the System<br/>8.6 Searching for Personal Profile of a Scientist - An Example<br/>8.6.1 Construction of Scientist's Profile<br/>8.6.2 Example Data Flow<br/>8.6.3 Set of Types<br/>8.6.4 System Environment and Structure<br/>8.6.5 Agents, Their Actions and Their Goais<br/>8.6.6 System Resources<br/>8.7 Conclusions<br/>Emerging Cooperation in the Spatial IPD with Reinforcement<br/>Learning and Coalitions<br/>Ana Peleteiro, Juan C. Burguillo, Ana L. Bazzan<br/>9.1 Introduction<br/>9.2 Related Work<br/>9.3 Prisoner's Dilemma<br/>9.4 The Game<br/>9.4.1 Spatial Distribution<br/>9.4.2 Basic Game Rules<br/>9.4.3 Agent Roles<br/>9.4.4 Scenarios and Agent Actions<br/>9.5 Reinforcement Learning Algorithms<br/>9.5.1 Q-Leaming (QL)<br/>9.5.2 Leeiming Automata (LA)<br/>9.5.3 Action Selection and States<br/>9.6 Scenarios<br/>9.7 Results Using the Coordination Game<br/>9.7.1 Scenario without Coalitions<br/>9.7.2 Scenario with Coalitions<br/>9.8 Results Using a Prisoner's Dilemma Approach<br/>9.8.1 Scenario without Coalitions .<br/>9.8.2 Scenario with Coalitions<br/>9.9 Conclusions and Future Work<br/>10 Evolutionary and Economic Agents in Complex Decision<br/>Systems<br/>Stephan Otto, Christoph Niemann<br/>10.1 Introduction<br/>10.2 Environments and Complex Decision Systems<br/>10.2.1 Environments<br/>10.2.2 Decision Systems<br/>10.3 Complex Decision Systems<br/>10.3.1 Software Agents<br/>10.3.2 Economic and Market-Based Models .<br/>10.3.3 Evolutionary Computation and Agents<br/>10.4 Case Studies.<br/>10.4.1 Hybrid Decision Systems<br/>10.4.2 Evolutionary Agents Optimize Supply Chain Structures<br/>10.4.3 Evolutionary Agents Optimize the p-median<br/>Problem<br/>10.5 Conclusion and Future Work<br/>11 On Reconfiguring Embedded Application Placement on Smart<br/>Sensing and Actuating Environments<br/>Nikos Tziritas, Samee Ullah Khan, Thanasis Loukopoulos<br/>11.1 Introduction<br/>11.1.1 Application Model<br/>11.1.2 Motivation<br/>11.1.3 Related Work and Contributions<br/>11.2 Problem Definition<br/>11.2.1 System Model<br/>11.2.2 Problem Formulation<br/>11.3 Algorithms<br/>11.3.1 The APR Problem with 2 Nodes<br/>11.3.2 The Agent Exchange Algorithm<br/>11.3.3 Extending to N Nodes<br/>11.3.4 Greedy Algorithmic Approach .<br/>11.4 Experiments<br/>11.4.1 Experimental Setup<br/>11.4.2 Comparison against the Optimal<br/>11.4.3 Experiments with a Larger Network<br/>11.4.4 Discussion<br/>11.5 Conclusions<br/>12 A Game Theoretic Approach to Dynamic Network Formation in<br/>Market-Oriented Resource Providing Networks<br/>Yutaka Okaie, Tadashi Nakano<br/>12.1 Introduction<br/>12.2 Network Formation Game Example<br/>12.3 The Model<br/>12.3.1 Agents<br/>12.3.2 Platforms<br/>12.4 Simulation Experiments<br/>12.4.1 Simulation Algorithms<br/>12.4.2 Default Simulation Configurations<br/>12.4.3 Simulation Results: Simple Scenario<br/>12.4.4 Simulation Results: Realistic Scenario<br/>12.5 Theoretical Analysis<br/>12.5.1 Edgeless Topologies<br/>12.5.2 Fully Connected Topologies<br/>12.5.3 i/-Regular Topologies<br/>12.5.4 Hub Topologies<br/>12.5.5 Summary of Theoretical Analysis<br/>12.6 Related Work<br/>12.7 Conclusion<br/>I<br/>13 Distributed Evolutionary Algorithm Using the MapReduce<br/>Paradigm - A Case Study for Data Compaction Problem<br/>Doina Logofatu, Manfred Gruber, Dumitru (Dan) Dumitrescu<br/>13.1 Introduction<br/>13.2 Problem Description<br/>13 3 Recent Work<br/>13.4 Parallel Evolutionary Algorithm Using MapReduce<br/>11.3 Algorithms<br/>11.3.1 The APR Problem with 2 Nodes<br/>11.3.2 The Agent Exchange Algorithm<br/>11.3.3 Extending to N Nodes<br/>11.3.4 Greedy Algorithmic Approach<br/>11.4 Experiments<br/>11.4.1 Expenmeniai ociup<br/>11.4.2 Comparison against the Optimal.<br/>11.4.3 Experiments with a Larger Netwoiis..<br/>11.4.4 Discussion<br/>11.5 Conclusions<br/>12 A Game Theoretic Approach to Dynamic Network Formation In<br/>Market-Oriented Resource Providing Networks<br/>Yutaka Okaie, Tadashi Nakano<br/>12.1 Introduction<br/>12.2 Network Formation Game Example<br/>12.3 The Model<br/>12.3.1 Agents<br/>12.3.2 Platforms<br/>12.4 Simulation Experiments<br/>12.4.1 Simulation Algorithms.<br/>12 4.2 Default Simulation Configurations<br/>12A.3 Simulation Results: Simple Scenario<br/>12.4.4 Simulation Results: Realistic Scenario<br/>12.5 Theoretical Analysis.<br/>12.5.1 Edgeless Topologies.<br/>12.5.2 Fully Connected Topologies<br/>12.5.3 rf-Regular Topologies<br/>12.5.4 Hub Topologies.<br/>12.5.5 Summary of Theoretical Analysis<br/>12.6 Related Work<br/>12.7 Conclusion<br/>13 Distributed Evoiutionary Algorithm Using the MapReduee<br/>piradigm - A Case Study for Data CompacUon Problem<br/>Doina Ugoato, Manfred Graber, Dumitn. (Dan) Dumitrescu<br/>13.1 Introduction<br/>13.2 Problem Description<br/>13 3 Recent Work<br/>13:4 Parallel Evolutionary Algonthm Using MapReduee<br/>13.5 Implementation Details<br/>13.6 Experimental Results and Statistical Tests<br/>13.7 Conclusions and Future Work .<br/>14 Virtual Accelerated Life Testing of Complex Systems<br/>Michael T. Todinov<br/>14.1 Introduction<br/>14.1.1 Arrhenius Stress-Life Relationship and Arrhenius-TVpe<br/>Acceleration Life Models<br/>14.1.2 Inverse Power Law Relationship (IPL) and IPL-TVpe<br/>Acceleration Life Models<br/>14.1.3 Eyring Stress-Life Relationship and Eyring-Type<br/>Acceleration Life Models<br/>14.1.4 A Motivation for the Proposed Method<br/>14.2 Limitations of Available Analytical Methods for Determining<br/>the Reliability of Large and Complex Systems<br/>14.3 Efficient Representation of Reliability Networks with Complex<br/>Topology and a Large Number of Components<br/>14.3.1 Representing the Topology of a Complex Reliability<br/>Network by an Array of Pointers to Dynamic Arrays<br/>14.3.2 Updating the Link Arrays after a Component<br/>Failure<br/>14.4 Existence of Paths to Each End Node in a Complex Reliability<br/>Network Represented by Adjacency Arrays and Link Arrays<br/>14.5 Accelerated Time to Failure of a Complex System<br/>14.6 A Software Tool<br/>14.7 A Solved Test Example.<br/>14.8 Conclusions<br/>15 Alvis - Modelling Language for Concurrent Systems<br/>Marcin Szpyrka, Piotr Matyasik, Rafal Mr6wka<br/>15.1 Introduction<br/>15.2 Related Works<br/>15.3 Communication Diagrams<br/>15.4 Language Statements<br/>15.5 System Layers<br/>15.6 Rule-Based Systems<br/>15.7 Alvis Model Example<br/>15.8 Agent and Model State<br/>15.9 Summary |
