Formal framework for distributed swarm computing: abstract model and properties
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Swarm computing is an emerging computing paradigm suitable for solving difficult optimization problems by employing a nature-inspired search for solutions. A set of entities called swarm entities populates a digital space that emulates a real physical environment. The digital space acts as a container of swarm entities by providing the basic functionalities for the entities management including processing and migration. Swarm entities are modeled as computational objects that follow a specific set of behavioral laws of natural inspiration. The entities are organized as a swarm, i.e., they coordinate, collaborate and act by exchanging information either directly or indirectly via the environment, seeking to solve a difficult computational optimization problem. Our main research result reported in this paper is the proposal of a new formal computational model of a generic distributed framework for swarm computing. Our model captures the basic computational properties of the swarm using the formal language of Finite State Process algebra. The proposed model is simple, clear and abstract, i.e., technology independent. It can serve as a starting basis for further refinement and subsequent development of new concurrent and/or distributed implementations using the available distributed computing technologies.
KeywordsSwarm computing Formal model Process algebra
Compliance with Ethical Standards
Conflict of interest
Amelia Bădică and Costin Bădică declares that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Antuña L, Araiza-Illan D, Campos S, Kerstin E (2015) Symmetry reduction enables model checking of more complex emergent behaviours of swarm navigation algorithms. In: Dixon C, Tuyls K (eds) 16th annual conference: towards autonomous robotic systems. TAROS 2015, Lecture notes in computer science, vol 9287. Springer, Berlin, pp 26–37Google Scholar
- Bădică A, Bădică C (2010) Specification and verification of an agent-based auction service. In: Papadopoulos GA, Wojtkowski W, Wojtkowski G, Wrycza S, Zupancic J (eds) Information systems development. Springer, New York, pp 239–248Google Scholar
- Bădică A, Bădică C, Brezovan M (2015) FSP modeling of a generic distributed swarm computing framework. In: Novais P, Camacho D, Analide C, El Fallah-Seghrouchni A, Bădică C (eds) Intelligent distributed computing IX. 9th international symposium on intelligent distributed computing—IDC’2015, vol 616, Springer, Berlin, pp 177–186Google Scholar
- Cicirelli F, Forestiero A, Giordano A, Mastroianni C (2016) Transparent and efficient parallelization of swarm algorithms. ACM Trans Auton Adapt Syst 11(2), Article 14, ACMGoogle Scholar
- Gjondrekaj E, Loreti M, Pugliese R, Tiezzi F, Pinciroli C, Brambilla M, Birattari M, Dorigo M (2012) Towards a formal verification methodology for collective robotic systems. In: Aoki T, Taguchi K (eds), Formal methods and software engineering. 14th international conference on formal engineering methods, ICFEM 2012, , Lecture notes in computer science, vol 7635, Springer, Berlin, pp 54–70Google Scholar
- Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, ICNN’1995, vol 4, pp 1942–1948Google Scholar
- Laibinis L, Troubitsyna E, Graja Z, Migeon F, Kacem AH (2014) Formal modelling and verification of cooperative ant behaviour in event-B. In: Giannakopoulou D, Salaün G (eds) Software engineering and formal methods: 12th international conference, SEFM 2014, Lecture notes in computer science. Springer, Berlin, pp 363–377Google Scholar
- Magee J, Kramer J (2006) Concurrency state models and java programs. World wide series in computer science, 2nd edn. Wiley, New YorkGoogle Scholar
- Petcu D (2002) Parallel explicit state reachability analysis and state space construction. In: Proceedings of the second international symposium on parallel and distributed computing, ISPDC’2003, Ljubljana, Slovenia, pp 207–214Google Scholar