A-Team Middleware on a Cluster

Czarnowski, Ireneusz; Jędrzejowicz, Piotr; Wierzbowska, Izabela

doi:10.1007/978-3-642-01665-3_77

Cited by 11 publications

(5 citation statements)

References 11 publications

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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: 98%

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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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Section: Related Workmentioning

confidence: 98%

“…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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“…In other words, approaches based on the parallel computing strategy exploit parallel processing for scaling up machine learning algorithms to work with large datasets [116], [180]. Several approaches focused on parallelizing learning algorithms have been proposed so far (see, for example, [48], [53], [55], [60], [63], [114], [204]). …”

Section: Strategies Of the Distributed Learningmentioning

confidence: 99%

“…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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“…eJABAT described in Subsection 2.1, -cluster Holk: a cluster of computers with Linux at the Academic Computer Centre TASK [14].…”

Section: Enviromentsmentioning

confidence: 99%

JABAT Middleware as a Tool for Solving Optimization Problems

Barbucha

Czarnowski

Jędrzejowicz

et al. 2010

Lecture Notes in Computer Science

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Abstract. JABAT supports designing and implementing A-Team architectures for solving difficult optimization problems. This paper presents several applications of JABAT as a tool for solving such problems. List of implementations and extensions of JABAT shows how useful and flexible the system can be. The paper summarises experiences of authors gained while developing various A-Teams. Some conclusions concerning such details of the A-Team model like the composition of the team of agents, the choice of rules determining how the agents interact with the population of solutions, or how synchronisation or cooperation of agents influence the quality of results are offered.

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A-Team Middleware on a Cluster

Cited by 11 publications

References 11 publications

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

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

Distributed Learning with Data Reduction

JABAT Middleware as a Tool for Solving Optimization Problems

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