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)