scholarly journals Multi-Agent System Supporting Automated Large-Scale Photometric Computations

Entropy ◽  
2016 ◽  
Vol 18 (3) ◽  
pp. 76 ◽  
Author(s):  
Adam Sȩdziwy ◽  
Leszek Kotulski
Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2992
Author(s):  
Niharika Singh ◽  
Irraivan Elamvazuthi ◽  
Perumal Nallagownden ◽  
Gobbi Ramasamy ◽  
Ajay Jangra

Microgrids help to achieve power balance and energy allocation optimality for the defined load networks. One of the major challenges associated with microgrids is the design and implementation of a suitable communication-control architecture that can coordinate actions with system operating conditions. In this paper, the focus is to enhance the intelligence of microgrid networks using a multi-agent system while validation is carried out using network performance metrics i.e., delay, throughput, jitter, and queuing. Network performance is analyzed for the small, medium and large scale microgrid using Institute of Electrical and Electronics Engineers (IEEE) test systems. In this paper, multi-agent-based Bellman routing (MABR) is proposed where the Bellman–Ford algorithm serves the system operating conditions to command the actions of multiple agents installed over the overlay microgrid network. The proposed agent-based routing focuses on calculating the shortest path to a given destination to improve network quality and communication reliability. The algorithm is defined for the distributed nature of the microgrid for an ideal communication network and for two cases of fault injected to the network. From this model, up to 35%–43.3% improvement was achieved in the network delay performance based on the Constant Bit Rate (CBR) traffic model for microgrids.


2018 ◽  
Vol 7 (3.13) ◽  
pp. 38
Author(s):  
S A. Khovanskov ◽  
K E. Rumyantsev ◽  
V S. Khovanskova

Currently, there are many different approaches for organization of the distributed calculations in computer network technology grid, metacomputing (BOINC, PVM, and others).  The main drawback of most existing approaches is that they are designed to create centralized distributed computing systems. In this article we propose to organize the solution of such problems as multivariate modeling, through the creation of distributed computations in computer networks based on decentralized multi-agent system. When used as a computing environment a computer network on a large scale can cause threats to the security of distributed computing from the intruders. One of these threats is getting the calculation about the result by the attacker. A false result can leads in the modeling process to adopt is not optimal or wrong decisions. We developed a method of protecting distributed computing from the threat of receiving false result.  


2012 ◽  
Vol 6-7 ◽  
pp. 778-782 ◽  
Author(s):  
Yan Ling Wang

Order to improve competitiveness in the logistics supply chain management of fisheries has become an increasingly important fishery enterprise, especially fisheries major retail enterprises. Logistics supply chain management has become part of the agenda of senior management in fisheries production and the retail industry to improve organizational efficiency and improve customer value, better use of resources and improve profitability and achieve organizational goals. In this article, the fisheries supply chain coordination problems. Multi-agent system, it can effectively deal with the distributed large-scale data, the coordination of fisheries development of retail logistics supply chain, warehouse and cross-pier is open, in this paper operation. To meet the individual needs of different participants in the proposed multi-agent system architecture for an efficient, responsive logistics supply chain coordination methods. The proposed multi-agent system can adaptively change over time, when the new organization is involved and the other disappeared. Proposed multi-agent systems, improve the level of the fisheries supply chain flexibility, the more sensitive members of the fisheries supply chain.


2002 ◽  
Vol 14 (4) ◽  
pp. 371-385 ◽  
Author(s):  
LEE MCCAULEY ◽  
STAN FRANKLIN

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