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Authors: Petr Skobelev 1 ; Igor Mayorov 2 ; Sergey Kozhevnikov 3 ; Alexander Tsarev 2 and Elena Simonova 4

Affiliations: 1 Software Engineering Company «Smart Solutions» and Ltd., Russian Federation ; 2 Smart Solutions, Ltd and Samara State Technical University, Russian Federation ; 3 SEC “Smart Solutions”, Russian Federation ; 4 Samara State Aerospace University, Russian Federation

Keyword(s): distributed problem solving, multi-agent technology, adaptive scheduling and optimization, swarm intelligence, unstable equilibrium, not-linear behavior, simulation, real-time.

Related Ontology Subjects/Areas/Topics: Agent Platforms and Interoperability ; Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Computational Intelligence ; Distributed and Mobile Software Systems ; Distributed Problem Solving ; Enterprise Information Systems ; Evolutionary Computing ; Formal Methods ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Multi-Agent Systems ; Planning and Scheduling ; Self Organizing Systems ; Simulation and Modeling ; Soft Computing ; Software Engineering ; Symbolic Systems

Abstract: In this paper modern methods of scheduling and resource optimization based on the holonic approach and principles of “Swarm Intelligence” are considered. The developed classes of holonic agents and method of adaptive real time scheduling where every agent is connected with individual satisfaction function by the set of criteria and bonus/penalty function are discussed. In this method the plan is considered as a un-stable equilibrium (consensus) of agents interests in dynamically self-organized network of demands and supply agents. The self-organization of plan demonstrates a “swarm intelligence” by spontaneous autocatalitical reactions and other not-linear behaviours. It is shown that multi-agent technology provides a generic framework for developing and researching various concepts of “Swarm Intelligence” for real time adaptive event-driving scheduling and optimization. The main result of research is the developed approach to evaluate the adaptability of “Swarm Intelligence ” by measuring improve of value and transition time from one to another unstable state in case of disruptive events processing. Measuring adaptability helps to manage self-organized systems and provide better quality and efficiency of real time scheduling and optimization. This approach is under implementation in multi-agent platform for adaptive resource scheduling and optimization. The results of first experiments are presented and future steps of research are discussed. (More)

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Paper citation in several formats:
Skobelev, P.; Mayorov, I.; Kozhevnikov, S.; Tsarev, A. and Simonova, E. (2015). Measuring Adaptability of ”Swarm Intelligence” for Resource Scheduling and Optimization in Real Time. In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-074-1; ISSN 2184-433X, SciTePress, pages 517-522. DOI: 10.5220/0005276605170522

@conference{icaart15,
author={Petr Skobelev. and Igor Mayorov. and Sergey Kozhevnikov. and Alexander Tsarev. and Elena Simonova.},
title={Measuring Adaptability of ”Swarm Intelligence” for Resource Scheduling and Optimization in Real Time},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2015},
pages={517-522},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005276605170522},
isbn={978-989-758-074-1},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Measuring Adaptability of ”Swarm Intelligence” for Resource Scheduling and Optimization in Real Time
SN - 978-989-758-074-1
IS - 2184-433X
AU - Skobelev, P.
AU - Mayorov, I.
AU - Kozhevnikov, S.
AU - Tsarev, A.
AU - Simonova, E.
PY - 2015
SP - 517
EP - 522
DO - 10.5220/0005276605170522
PB - SciTePress