Abstract
Scientists working in the area of distributed function optimization have to deal with a huge variety of optimization techniques and algorithms. Most of the existing research in this domain makes use of tightly-coupled systems that either have strict synchronization requirements or completely rely on a central server, which coordinates the work of clients and acts as a state repository. Quite recently, the possibility to perform such optimization tasks in a P2P decentralized network of solvers has been investigated and explored, leading to promising results. In order to improve and ease this newly addressed research area, we designed and developed p2poem (P2P Optimization Epidemic Middleware), that aims at bridging the gap between the issues related to the design and deployment of large-scale P2P systems and the need to easily deploy and execute optimization tasks in such a distributed environment.
Access this article
Rent this article via DeepDyve
Similar content being viewed by others
Notes
References
Addis B, Cassioli A, Locatelli M, Schoen F (2008) Global optimization for the design of space trajectories. Optimization online eprint archive http://www.optimization-online.org/DB_HTML/2008/11/2150.html. Accessed 21 June 2012
Alba E, Luque G, Nieto JM, Ordóñez G, Leguizamón G (2007) MALLBA: a software library to design efficient optimization algorithms. IJICA 1(1):74–85
Arenas MG, Collet P, Eiben AE, Jelasity M, Merelo JJ, Paechter B, Preuß M, Schoenauer M (2002) A framework for distributed evolutionary algorithms. In: Parallel Problem Solving from Nature—PPSN VII, vol 2439 of LNCS. Springer, pp 665–675
Battiti R, Brunato M, Mascia F (2008) Reactive search and intelligent optimization. Springer Publishing Company, Incorporated
Berntsson J (2005) G2DGA: an adaptive framework for internet-based distributed genetic algorithms. In: Proc. of GECCO ’05. ACM, New York, pp 346–349
Biazzini M, Bánhelyi B, Montresor A, Jelasity M (2009) Distributed hyper-heuristics for real parameter optimization. In: Proc. of GECCO’09. Montreal, Québec, Canada, pp 1339–1346
Biazzini M, Bánhelyi B, Montresor A, Jelasity M (2009) Peer-to-Peer optimization in large unreliable networks with branch-and-bound and particle swarms. In: Applications of evolutionary computing. Springer, pp 87–92
Biazzini M, Montresor A (2010) Gossiping differential evolution: a decentralized heuristic for function optimization in P2P networks. In: Proceedings of the 16th International Conference on Parallel and Distributed Systems (ICPADS’10)
Biazzini M, Montresor A (2011) p2poem. http://p2poem.sf.net. Accessed 21 June 2012
Biazzini M, Montresor A, Brunato M (2008) Towards a decentralized architecture for optimization. In: Proc. of IPDPS’08. Miami, FL
Brunato M, Battiti R, Pasupuleti S (2006) A memory-based rash optimizer. In: Proc. of AAAI-06 workshop on heuristic search, memory based heuristics and their applications. Boston, MA
Burke E, Kendall G, Newall J, Hart E, Ross P, Schulenburg S (2003) Hyper-heuristics: an emerging direction in modern search technology. In: Handbook of metaheuristics. Springer, pp 457–474
Cahon S, Melab N, Talbi E-G (2004) Paradiseo: a framework for the reusable design of parallel and distributed metaheuristics. Journal of Heuristics 10(3):357–380
Demers A et al (1987) Epidemic algorithms for replicated database maintenance. In: Proc. of the 6th ACM symposium on Principles of Distributed Computing (PODC’87). ACM Press, pp 1–12
Desell T, Magdon-Ismail M, Szymanski B, Varela C, Newberg H, Anderson D (2010) Validating evolutionary algorithms on volunteer computing grids. In: Eliassen F, Kapitza R (eds) Distributed applications and interoperable systems, vol 6115 of lecture notes in computer science. Springer, Berlin/Heidelberg, pp 29–41
Gagne C, Parizeau M, Dubreuil M (2003) Distributed Beagle: an environment for parallel and distributed evolutionary computations. In: Proc. of the 17th int. symposium on high performance computing systems and applications. NRC Research Press, Sherbrooke, Québec, Canada
Gomes CP, Selman B (2001) Algorithm portfolios. Artif Intell 126(1–2):43–62
Hidalgo JI, Lanchares J, Fernández de Vega F, Lombrana D (2007) Is the island model fault tolerant? In: GECCO ’07. ACM, New York, pp 2737–2744
Jelasity M, Montresor A, Babaoglu O (2005) Gossip-based aggregation in large dynamic networks. ACM Trans Comput Syst 23(3):219–252
Jelasity M, Voulgaris S, Guerraoui R, Kermarrec A-M, van Steen M (2007) Gossip-based peer sampling. ACM Trans Comput Syst 25(3):8
Jiménez Laredo J, Lombraña González D, Fernández de Vega F, García Arenas M, Merelo Guervós J (2011) A Peer-to-Peer approach to genetic programming. In: Silva S, Foster J, Nicolau M, Machado P, Giacobini M (eds) Genetic programming, vol 6621 of lecture notes in computer science. Springer, Berlin/Heidelberg, pp 108–117
Kennedy J, Eberhart RC (1995) Particle swarm optimization. IEEE int. conf. neural networks, pp 1942–1948
Kesselman C, Foster I (1999) The Grid: blueprint for a new computing infrastructure. Morgan Kaufmann
Laredo J, Castillo P, Mora A, Merelo J (2008) Exploring population structures for locally concurrent and massively parallel evolutionary algorithms. In: IEEE Congress on Evolutionary Computation, 2008. (IEEE World Congress on Computational Intelligence), pp 2605–2612
Laredo JLJ, Castillo PA, Mora AM, Merelo JJ, Fernandes C (2008) Resilience to churn of a Peer-to-Peer evolutionary algorithm. Int J High Perform Syst Archit 1(4):260–268
Laredo JLJ, Eiben AE, Schoenauer M, Castillo PA, Mora AM, de Vega FF, Guervós JJM (2007) Self-adaptive gossip policies for distributed population-based algorithms. CoRR, abs/cs/0703117
Laredo JLJ, Eiben EA, van Steen M, Castillo PA, Mora AM, Merelo JJ (2008) P2P evolutionary algorithms: a suitable approach for tackling large instances in hard optimization problems. In: Proc. of Euro-Par, vol 5168 of LNCS. Springer-Verlag, pp 622–631
Maron O, Moore AW (1997) The racing algorithm: model selection for lazy learners. Artif Intell Rev 11:193–225
Melab N, Mezmaz M, Talbi E-G (2005) Parallel hybrid multi-objective island model in Peer-to-Peer environment. In: IPDPS ’05: proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS’05)—Workshop 6. IEEE Computer Society, Washington, p 190.2
Montresor A (2011) Cloudware. http://cloudware.sf.net. Accessed 21 June 2012
Montresor A, Jelasity M (2009) Peersim: a scalable P2P simulator. In: Proc. of the 9th Int. conference on Peer-to-Peer (P2P’09). Seattle, WA, pp 99–100
Pittel B (1987) On spreading a rumor. SIAM J Appl Math 47(1):213–223
Scriven I, Lewis A, Ireland D, Lu J (2008) Distributed multiple objective particle swarm optimisation using Peer-to-Peer networks. In: IEEE Congress on Evolutionary Computation (CEC)
Scriven I, Lewis A, Mostaghim S (2009) Dynamic search initialisation strategies for multi-objective optimisation in Peer-to-Peer networks. IEEE Congress on Evolutionary Computation, CEC ’09, pp 1515–1522
Storn R, Price K (1997) Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359
Talbi EG (2006) Parallel combinatorial optimization. John Wiley
Wickramasinghe WRMUK, van Steen M, Eiben AE (2007) Peer-to-Peer evolutionary algorithms with adaptive autonomous selection. In: Proc. of GECCO’07. ACM Press, New York, pp 1460–1467
Author information
Authors and Affiliations
Corresponding author
Additional information
Alberto Montresor is supported by the Italian MIUR Project Autonomous Security, sponsored by the PRIN 2008 Programme.
Rights and permissions
About this article
Cite this article
Biazzini, M., Montresor, A. p2poem: Function optimization in P2P networks. Peer-to-Peer Netw. Appl. 6, 213–232 (2013). https://doi.org/10.1007/s12083-012-0152-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12083-012-0152-8