Abstract
The paper presents a distributed computing system that is based on evolutionary algorithms and utilizing a web browser on a client’s side. Evolutionary algorithm is coded in JavaScript language embedded in a web page sent to the client. The code is optimized with regards to the memory usage and communication efficiency between the server and the clients. The server side is also based on JavaScript language, as node.js server was applied. The proposed system has been tested on the basis of permutation flowshop scheduling problem, one of the most popular optimization benchmarks for heuristics studied in the literature. The results have shown, that the system scales quite smoothly, taking additional advantage of local search algorithm executed by some clients.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Talbi, E.-G.: Parallel combinatorial optimization. John Wiley and Sons (2006)
TOP500 Supercomputer (June 2012), http://www.top500.org/ (accessed October 10, 2012)
BOINC website, http://boinc.berkeley.edu/ (accessed October 10, 2012)
Merelo, J.J., García, A.M., Laredo, J.L.J., Lupión, J., Tricas, F.: Browser-based distributed evolutionary computation: performance and scaling behaviour. In: Proceedings of the 2007 GECCO, pp. 2851–2858. ACM, New York (2007)
Jenkin, N.: Parasitic JavaScript, COMP520-08 Report, University of Waikato, New Zealand (2008)
Barabási, A.-L., Freeh, V.W., Jeong, H., Brockman, J.: Parasitic computing. Nature 412, 894–897 (2001)
Pensini, M.P., Mauri, G., Gardin, F.: Flowshop and TSP. In: Mündemann, F.W., Becker, J.D., Eisele, I. (eds.) Parallelism, Learning, Evolution. LNCS, vol. 565, pp. 157–182. Springer, Heidelberg (1991)
Ponnambalam, S.G., Jawahar, N., Chandrasekaran, S.: Discrete Particle Swarm Optimization Algorithm for Flowshop Scheduling. In: Lazinica, A. (ed.) Particle Swarm Optimization. InTech (2009)
Kouki, S., Ladhari, T., Jemni, M.: A Parallel Distributed Algorithm for the Permutation Flow Shop Scheduling Problem. In: Hsu, C.-H., Yang, L.T., Park, J.H., Yeo, S.-S. (eds.) ICA3PP 2010, Part I. LNCS, vol. 6082, pp. 328–337. Springer, Heidelberg (2010)
Cung, V.D., Martins, S.L., Ribeiro, C.C., Roucairol, C.: Strategies for the parallel implementation of metaheuristics, Essays and Surveys in Metaheuristics, pp. 263–308. Kluwer Academic Publishers (2002)
Syswerda, G.: Schedule optimization using genetic algorithms. In: Davis, L. (ed.) Handbook of Genetic Algorithms, pp. 332–349. Van Nostrand Reinhol, New York (1991)
Taillard, E.D.: Benchmarks for basic scheduling problems. European Journal of Operational Research 64, 278–285 (1993)
Mladenovic, N., Hansen, P.: Variable neighborhood search. Computers and Operations Research 24(11), 1097–1100 (1997)
Zobolas, G.I., Tarantilis, C.D., Ioannou, G.: Minimizing makespan in permutation flow shop scheduling problems using a hybrid metaheuristic algorithm. Comput. Oper. Res. 36(4), 1249–1267 (2009)
http://nodejs.org/ (accessed November 20, 2012)
Tilkov, S., Vinoski, S.: Node.js: Using JavaScript to Build High-Performance Network Programs. IEEE Internet Computing, 80–83 (2010)
Introducing Socket.IO, http://socket.io/ (accessed November 20, 2012)
Scaling node.js to 100k concurrent connections, http://blog.caustik.com/2012/04/08/scaling-node-js-to-100k-concurrent-connections/ (accessed November 20, 2012)
Multi-Process Node.js: Motivations, Challenges and Solutions, http://www.infoq.com/articles/multi-core-node-js/ (accessed November 20, 2012)
V8 JavaScript Engine, http://code.google.com/p/v8/ (accessed November 20, 2012)
http://shootout.alioth.debian.org/ (accessed November 20, 2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Duda, J., Dłubacz, W. (2013). Distributed Evolutionary Computing System Based on Web Browsers with JavaScript. In: Manninen, P., Öster, P. (eds) Applied Parallel and Scientific Computing. PARA 2012. Lecture Notes in Computer Science, vol 7782. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36803-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-36803-5_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36802-8
Online ISBN: 978-3-642-36803-5
eBook Packages: Computer ScienceComputer Science (R0)