Wyniki wyszukiwania - BazTech

1

Energy redistribution in autonomous hybridization of agent-based computing

Godzik Mateusz

Computer Science

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2021

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T. 22 (3)

345–365

EN

Evolutionary multi-agent systems (EMAS) are very good at dealing with diffi cult, multi-dimensional problems. Research is currently underway to improve this algorithm, giving agents even more freedom not only to solve the problem, but also to make decisions about the behavior of the algorithm. One way is to hybridize this algorithm with other existing algorithms to create the Hybrid Evolutionary Multi Agent-System (HEMAS). Unfortunately, such connections generate problems in the form of unbalanced agent energy levels. One solution is to use an agent energy redistribution operator. The article presents three different proposals for such redistribution operators, compared them with each other and selected the best based on the results of numerous experiments.

2

Collectively intelligent prediction in evolutionary multi-agent system

Kijak J., Martyna P., Byrski A., Faber Ł., Piętak K., Kisiel-Dorohinicki M.

International Journal of Microelectronics and Computer Science

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2017

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Vol. 8, nr 3

85--96

EN

In the paper a summary of our previously realized and published work connected with constructing collective intelligent evolutionary multi-agent systems for time series prediction, based on multi-layered perceptrons is shown. Besides recalling our past papers, we describe the whole concept, present an implementation in a contemporary, componentoriented software framework AgE 3.0 and we conduct a number of experiments, finding different optimal parametrization for the considered instances of the problems (popular Mackey-Glass chaotic time series). The paper may be useful for a practitioner willing to use our meatheuristic algorithm (EMAS) along with the idea of collective agent-based system in order to realize prediction tasks.

3

Parallel patterns for agent-based evolutionary computing

Stypka J., Anielski P., Mentel S., Krzywicki D., Turek W., Byrski A., Kisiel-Dorohinicki M.

Computer Science

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2016

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Vol. 17 (1)

83--98

EN

Computing applications such as metaheuristics-based optimization can greatly benefit from multi-core architectures available on modern supercomputers. In this paper, we describe an easy and efficient way to implement certain population-based algorithms (in the discussed case, multi-agent computing system) on such runtime environments. Our solution is based on an Erlang software library which implements dedicated parallel patterns. We provide technological details on our approach and discuss experimental results.

4

Tuning of agent-based computing

Byrski A.

Computer Science

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2013

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Vol. 14 (3)

491--512

EN

In this paper, an Evolutionary Multi-agent system-based computing process is subjected to a detailed analysis of its parameters in order to establish a base for a better understanding of the meta-heuristics from the practitioner’s point of view. After reviewing the concepts of EMAS and its immunological variant, a series of experiments is shown, and results of the influence of the search outcomes by certain parameters is discussed.