JADE-Based A-Team as a Tool for Implementing Population-Based Algorithms

Barbucha, Dariusz; Czarnowski, Ireneusz; Jędrzejowicz, Piotr; Ratajczak, E.; Wierzbowska, Izabela

doi:10.1109/isda.2006.31

Cited by 20 publications

(5 citation statements)

References 13 publications

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“…The above middleware platform is called JADE-Based A-Team Environment (JABAT). Its subsequent versions, developed with the participation of the author, were proposed in [11], [13], [60], [112]. The platform offers a generic A-Team architecture allowing users to execute the population learning algorithm with some default or user-defined optimization procedures implemented as agents within an asynchronous team of agents.…”

Section: A-team Implementationmentioning

confidence: 99%

Distributed Learning with Data Reduction

Czarnowski

2011

Lecture Notes in Computer Science

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Abstract. The work deals with the distributed machine learning. Distributed learning from data is considered to be an important challenge faced by researchers and practice in the domain of the distributed data mining and distributed knowledge discovery from databases. Currently, learning from data is recognized as one of the most widely investigated paradigms of machine learning. At the same time it is perceived as a difficult and demanding computational problem. Even more complex and still to a large extent open is learning from the distributed data. One of the approaches suitable for learning from the geographically distributed data is to select from the local databases relevant local patterns, called also prototypes. Such prototypes are selected using some specialized data reduction methods. The dissertation contains an overview of the problem of learning classifiers from data, followed by a discussion of the distributed learning. The above includes the problem formulation and the state-of-the-art review. Next, data reduction, approaches, techniques and algorithms are discussed. The central part of the dissertation proposes an agent-based distributed learning framework. The idea is to carry-out data reduction in parallel in separate locations, employing specialized software agents. The process ends when locally selected prototypes are moved to a central site and merged into the global knowledge model. The following part of the work contains the results of an extensive computational experiment aiming at validation of the proposed approach. Finally, conclusions and suggestions for further research are formulated.

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Section: A-team Implementationmentioning

confidence: 99%

Distributed Learning with Data Reduction

Czarnowski

2011

Lecture Notes in Computer Science

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“…Moreover, there are even fewer proposals that utilize recent advances of multi-agent systems middleware for ACObased TSP ( [6]). Nevertheless, we could find references to multi-agent approaches to ACO algorithms for other combinatorial optimization problems ( [8], [1], [3]). …”

Section: Related Workmentioning

confidence: 99%

“…Papers [1] and [3] propose JABAT -a JADE-based middleware for agent teams. JABAT supports distributed implementation and collaboration of population-based optimization algorithms.…”

Section: Related Workmentioning

confidence: 99%

Distributed Multi-agent System for Solving Traveling Salesman Problem Using Ant Colony Optimization

Ilie

Bǎdicǎ

2010

Studies in Computational Intelligence

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Abstract. In this paper we present our approach and initial results for solving the Traveling Salesman Problem using Ant Colony Optimization on distributed multiagent architectures. We introduce the framework including underlying architecture design, algorithms and experimental setup. Then we present initial scalability results that we obtained with the implementation of the framework using JADE multi-agent platform on a high-speed cluster network.

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“…e-JABAT makes it possible to focus only on defining these new elements, while the processes of communication and population management procedures will still work. More detailed information about extending the functionality of JABAT can be found in [1].…”

Section: Flexibility Of E-jabatmentioning

confidence: 99%