Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Deployment of Agent Technologies in Industrial Applications
DIS '06 Proceedings of the IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications
Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology)
Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology)
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Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A multiobjectivization approach for vehicle routing problems
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Multiobjective prototype optimization with evolved improvement steps
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Multiobjective data clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Iterative prototype optimisation with evolved improvement steps
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Multiagent technology for fault tolerance and flexible control
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Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Timetable Synchronization of Mass Rapid Transit System Using Multiobjective Evolutionary Approach
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
PSO-Based Multiobjective Optimization With Dynamic Population Size and Adaptive Local Archives
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multi-agent system design and integration via agent development environment
Engineering Applications of Artificial Intelligence
Network-based strategies for signalised traffic intersections
International Journal of Systems, Control and Communications
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Multiagent systems consist of a collection of agents that directly interact usually via a form of message passing. Information about these interactions can be analyzed in an online or offline way to identify clusters of agents that are related. The first part of this paper is dedicated to a formal definition of a proposed dynamic model for agent clustering and experimental results that demonstrate applicability of this novel approach. The main contribution is the ability to discover and visualize communication neighborhoods of agents at runtime, which is a novel approach not attempted so far. The second part of this paper deals with a static agent clustering problem where equally sized clusters with maximal intracluster communication among agents are sought in order to efficiently distribute agents across multiple execution units. The weakness of standard clustering approaches for solving this type of clustering problem is shown. First, these algorithms optimize the generated clustering with respect to just one criterion, and therefore, yield solutions with inferior quality relative to the other criteria. Second, the algorithms are deterministic; thus they can produce just a single solution for the given data. A multiobjective clustering approach based on an iterative optimization evolutionary algorithm called multiobjective prototype optimization with evolved improvement steps (mPOEMS) is proposed and its advantages are demonstrated. The most important observation is that mPOEMS produces numerous highquality solutions in a single run from which a user can choose the best one. The best solutions found by mPOEMS are significantly better than the solutions generated by the compared clustering algorithms.