A Comparative Study of Four Parallel and Distributed PSO Methods

We present four new parallel and distributed particle swarm optimization methods consisting in a genetic algorithm whose individuals are co-evolving swarms, an “island model”-based multi-swarm system, where swarms are independent and interact by means of particle migrations at regular time steps, an...

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Published in:	New generation computing Vol. 29; no. 2; pp. 129 - 161
Main Authors:	Vanneschi, Leonardo, Codecasa, Daniele, Mauri, Giancarlo
Format:	Journal Article
Language:	English
Published:	Heidelberg Verlag Omsha Tokio 01.04.2011
Subjects:	Artificial Intelligence Computer Hardware Computer Science Computer Systems Organization and Communication Networks Software Engineering/Programming and Operating Systems Parallel and Distributed Algorithms Optimization Swarm Intelligence
ISSN:	0288-3635, 1882-7055
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Abstract	We present four new parallel and distributed particle swarm optimization methods consisting in a genetic algorithm whose individuals are co-evolving swarms, an “island model”-based multi-swarm system, where swarms are independent and interact by means of particle migrations at regular time steps, and their respective variants enriched by adding a repulsive component to the particles. We study the proposed methods on a wide set of problems including theoretically hand-tailored benchmarks and complex real-life applications from the field of drug discovery, with a particular focus on the generalization ability of the obtained solutions. We show that the proposed repulsive multi-swarm system has a better optimization ability than all the other presented methods on all the studied problems. Interestingly, the proposed repulsive multi-swarm system is also the one that returns the most general solutions.
AbstractList	We present four new parallel and distributed particle swarm optimization methods consisting in a genetic algorithm whose individuals are co-evolving swarms, an “island model”-based multi-swarm system, where swarms are independent and interact by means of particle migrations at regular time steps, and their respective variants enriched by adding a repulsive component to the particles. We study the proposed methods on a wide set of problems including theoretically hand-tailored benchmarks and complex real-life applications from the field of drug discovery, with a particular focus on the generalization ability of the obtained solutions. We show that the proposed repulsive multi-swarm system has a better optimization ability than all the other presented methods on all the studied problems. Interestingly, the proposed repulsive multi-swarm system is also the one that returns the most general solutions.
Author	Vanneschi, Leonardo Codecasa, Daniele Mauri, Giancarlo
Author_xml	– sequence: 1 givenname: Leonardo surname: Vanneschi fullname: Vanneschi, Leonardo email: vanneschi@disco.unimib.it organization: Department of Informatics, Systems and Communication (D.I.S.Co.), University of Milano-Bicocca – sequence: 2 givenname: Daniele surname: Codecasa fullname: Codecasa, Daniele organization: Department of Informatics, Systems and Communication (D.I.S.Co.), University of Milano-Bicocca – sequence: 3 givenname: Giancarlo surname: Mauri fullname: Mauri, Giancarlo organization: Department of Informatics, Systems and Communication (D.I.S.Co.), University of Milano-Bicocca
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Cites_doi	10.1145/1543834.1543886 10.1016/j.amc.2006.07.026 10.1109/ICMAS.1998.699217 10.1109/CEC.2005.1554727 10.1155/2008/685175 10.1016/j.asoc.2009.06.013 10.1007/978-3-540-78761-7_62 10.1093/oso/9780195131581.001.0001 10.1023/A:1021873026259 10.1007/978-3-540-24653-4_50 10.1109/TEVC.2007.896686 10.1109/WKDD.2008.78 10.1007/11730095_3 10.1016/j.asoc.2007.01.010 10.1109/CIS.2007.95 10.1038/73439 10.1002/9780470612163 10.1109/CEC.2003.1299599 10.1109/ICNC.2007.337 10.1109/ICNC.2008.313 10.1145/1830483.1830487 10.1038/73432 10.1145/1809018.1809022 10.1155/2008/289564 10.1007/s10710-007-9040-z 10.1007/s11721-007-0002-0 10.1109/WKDD.2009.202 10.1016/j.eswa.2008.09.017
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References	Archetti, F., Giordani, I. and Vanneschi, L., “Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset,” Computers and Operations Research, 37, 8, pp. 1395–1405, 2010. Impact Factor: 1.789. DiosanL.OlteanM.“What else is evolution of pso telling us?”Journal of Artificial Evolution and Applications20081511210.1155/2008/289564 KennedyJ.PoliR.BlackwellT.“Particle swarm optimization: an overview”Swarm Intelligence200711335710.1007/s11721-007-0002-0 Kameyama, K., “Particle swarm optimization - a survey,” IEICE Transactions, 92-D, 7, pp. 1354–1361, 2009. Riget, J. and Vesterstrm, J., “A diversity-guided particle swarm optimizer - the arpso,” Technical report, Dept. of Comput. Sci., Aarhus Univ., Denmark, 2002. Wang, Y. and Yang, Y., “An interactive multi-swarm pso for multiobjective optimization problems,” Expert Systems with Applications, In press, 2008. On-line version available at http://www.sciencedirect.com. Kwong, H. and Jacob, C., “Evolutionary exploration of dynamic swarm behavior,” in IEEE Congress on Evolutionary Computation, CEC'03, IEEE Press, pp. 367–374, 2003. Poli, R., “Analysis of the publications on the applications of particle swarm optimisation,” J. Artif. Evol. App., 2008, 1, pp. 1–10, January 2008. Kennedy, J. and Eberhart, R. C., Swarm Intelligence, Morgan Kaufmann Publishers, 2001. Wu, Z. and Zhou, J., “A self-adaptive particle swarm optimization algorithm with individual coefficients adjustment,” in Proc. IEEE International Conference on Computational Intelligence and Security, CIS'07, IEEE Computer Society, pp. 133–136, 2007. Jiang, Y., Huang, W., Chen, L., “Applying multi-swarm accelerating particle swarm optimization to dynamic continuous functions,” in 2009 Second International Workshop on Knowledge Discovery and Data Mining, pp. 710–713, 2009. SherfU.“A gene expression database for the molecular pharmacology of cancer”Nat Genet200024323624410.1038/73439 Clerc, M. ed., Particle Swarm Optimization, ISTE, 2006. Kennedy, J. and Mendes, R., “Population structure and particle swarm performance,” in IEEE Congress on Evolutionary Computation, CEC'02, IEEE Computer Society, pp. 1671–1676, 2002. RossD.T.“Systematic variation in gene expression patterns in human cancer cell lines”Nat Genet200024322723510.1038/73432 Cagnoni, S., Vanneschi, L., Azzini, A. and Tettamanzi, A., “A critical assessment of some variants of particle swarm optimization,” in European Workshop on Bio-inspired algorithms for continuous parameter optimisation, EvoNUM'08, Springer Verlag, pp. 565–574, 2008. NiuB.ZhuY.HeX.WuH.“MCPSO: A multi-swarm cooperative particle swarm optimizer”Applied Mathematics and Computation200721851050106210.1016/j.amc.2006.07.026 Blackwell, T. and Branke, J., “Multi-swarm optimization in dynamic environments,” in EvoWorkshops (Raidl, G. R. et al. eds.), LNCS, Springer, pp. 489–500, 2004. Zhigljavsky, A. and Zilinskas, A., “Stochastic Global Optimization,” Springer Optimization and Its Applications, 9, Springer, 2008. ArchettiF.GiordaniI.VanneschiL.“Genetic programming for QSAR investigation of docking energy”Applied Soft Computing201010117018210.1016/j.asoc.2009.06.013 Lu, F.-Q., Huang, M., Ching, W.-K., Wang, X.-W. and Sun, X.-l., “Multi-swarm particle swarm optimization based risk management model for virtual enterprise,” in GEC '09: Proc. of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, New York, NY, USA, ACM, pp. 387–392, 2009. Blackwell, T. M., “Swarm music: improvised music with multi-swarms,” in Proc. of the 2003 AISB Symp. on Artificial Intelligence and Creativity in Arts and Science, pp. 41–49, 2003. FernándezF.TomassiniM.VanneschiL.“An empirical study of multipopulation genetic programming”Genetic Programming and Evolvable Machines.20034121521009.6857010.1023/A:1021873026259 Diosan, L. and Oltean, M., “Evolving the structure of the particle swarm optimization algorithms,” in EvoCOP'06, Springer Verlag, pp. 25–36, 2006. N. C. M. Project, National Cancer Institute, Bethesda MD, 2008. See http://genome-www.stanford.edu/nci60/. ValleY.D.VenayagamoorthyG.MohagheghiS.HernandezJ.HarleyR.“Particle swarm optimization: Basic concepts, variants and applications in power systems”IEEE Transactions on Evolutionary Computation200812217119510.1109/TEVC.2007.896686 Weka, A multi-task machine learning software developed by Waikato University, 2006. See http://www.cs.waikato.ac.nz/ml/weka. Vanneschi, L., Codecasa, D. and Mauri, G., “A study of parallel and distributed particle swarm optimization methods,” in Proc. of the 2nd workshop on Bio-inspired algorithms for distributed systems, BADS'10, New York, NY, USA, ACM, pp. 9–16, 2010. You, X., Liu, S. and Zheng, W., “Double-particle swarm optimization with induction enhanced evolutionary strategy to solve constrained optimization problems,” in IEEE International Conference on Natural Computing, ICNC'07, IEEE Computer Society, pp. 527–531, 2007. ArchettiF.MessinaE.LanzeniS.VanneschiL.“Genetic programming for computational pharmacokinetics in drug discovery and development”Genetic Programming and Evolvable Machines200784172610.1007/s10710-007-9040-z Srinivasan, D. and Seow, T. H., “Particle swarm inspired evolutionary algorithm (ps-ea) for multi-objective optimization problem,” in IEEE Congress on Evolutionary Computation, CEC03, IEEE Press, pp. 2292–2297, 2003. Vanneschi, L., Codecasa, D. and Mauri, G., “An empirical comparison of parallel and distributed particle swarm optimization methods,” in Proc. of the Genetic and Evolutionary Computation Conference, GECCO 2010 (Branke, J. et al. eds.), ACM Press, 2010. To appear. Shi, Y. H. and Eberhart, R., “A modified particle swarm optimizer,” in Proc. IEEE Int. Conference on Evolutionary Computation, IEEE Computer Society, pp. 69–73, 1998. Smola, A. J. and Scholkopf, B., “A Tutorial on Support Vector Regression,” Technical Report Technical Report Series - NC2-TR-1998-030, NeuroCOLT2, 1999. Bonabeau, E., Dorigo, M. and Theraulaz, G., Swarm Intelligence: From Natural to Artificial Systems (Santa Fe Institute Studies in the Sciences of Complexity), Oxford University Press, New York, NY, 1999. ArumugamM.S.RaoM.“On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (rms) variants for computing optimal control of a class of hybrid systems”Journal of Applied Soft Computing2008832433610.1016/j.asoc.2007.01.010 Vanneschi, L., “Theory and Practice for Efficient Genetic Programming,” Ph.D. thesis, Faculty of Sciences, University of Lausanne, Switzerland, 2004. RossS.M.Introduction to Probability and Statistics for Engineers and scientists2000NewYorkAcademic Press0942.62001 Suganthan, P., Hansen, N., Liang, J., Deb, K., Chen, Y., Auger, A. and Tiwari, S., “Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization,” Technical Report Number 2005005, Nanyang Technological University, 2005. White, T. and Pagurek, B., “Towards multi-swarm problem solving in networks,” in Proc. of Third International Conference on Multi-Agent Systems (ICMAS'98), IEEE Computer Society, pp. 333–340, 1998. Zhiming, L., Cheng, W. and Jian, L., “Solving constrained optimization via a modified genetic particle swarm optimization,” in Workshop on Knowledge Discovery and Data Mining, WKDD'08, IEEE Computer Society, pp. 217–220, 2008. Poli, R., “Analysis of the publications on the applications of particle swarm optimization,” Journal of Artificial Evolution and Applications, 2009, (in press). Kennedy, J. and Eberhart, R., “Particle swarm optimization,” in Proc. IEEE Int. conf. on Neural Networks, 4, IEEE Computer Society, pp. 1942–1948, 1995. Li, C. and Yang, S., “Fast multi-swarm optimization for dynamic optimization problems,” in ICNC '08: Proc. of the 2008 Fourth International Conference on Natural Computation, Washington, DC, USA, IEEE Computer Society, pp. 624–628, 2008. Liang, J. J. and Suganthan, P. N., “Dynamic multi-swarm particle swarm optimizer with local search,” in 2005 IEEE Congress on Evolutionary Computation, CEC 2005, 1, pp. 522–528, 2005. F. Fernández (102_CR12) 2003; 4 102_CR40 102_CR20 S.M. Ross (102_CR28) 2000 102_CR42 102_CR41 102_CR22 102_CR44 102_CR21 102_CR43 102_CR24 102_CR45 102_CR26 102_CR25 102_CR39 102_CR16 B. Niu (102_CR23) 2007; 2 102_CR38 F. Archetti (102_CR2) 2010; 10 102_CR19 102_CR18 F. Archetti (102_CR3) 2007; 8 Y.D. Valle (102_CR35) 2008; 12 102_CR31 U. Sherf (102_CR30) 2000; 24 102_CR33 M.S. Arumugam (102_CR4) 2008; 8 102_CR10 L. Diosan (102_CR11) 2008; 1 102_CR32 102_CR13 102_CR34 102_CR15 102_CR37 102_CR14 102_CR36 102_CR1 102_CR27 D.T. Ross (102_CR29) 2000; 24 J. Kennedy (102_CR17) 2007; 1 102_CR9 102_CR7 102_CR8 102_CR5 102_CR6
References_xml	– reference: Shi, Y. H. and Eberhart, R., “A modified particle swarm optimizer,” in Proc. IEEE Int. Conference on Evolutionary Computation, IEEE Computer Society, pp. 69–73, 1998. – reference: SherfU.“A gene expression database for the molecular pharmacology of cancer”Nat Genet200024323624410.1038/73439 – reference: Jiang, Y., Huang, W., Chen, L., “Applying multi-swarm accelerating particle swarm optimization to dynamic continuous functions,” in 2009 Second International Workshop on Knowledge Discovery and Data Mining, pp. 710–713, 2009. – reference: Wang, Y. and Yang, Y., “An interactive multi-swarm pso for multiobjective optimization problems,” Expert Systems with Applications, In press, 2008. On-line version available at http://www.sciencedirect.com. – reference: Archetti, F., Giordani, I. and Vanneschi, L., “Genetic programming for anticancer therapeutic response prediction using the NCI-60 dataset,” Computers and Operations Research, 37, 8, pp. 1395–1405, 2010. Impact Factor: 1.789. – reference: ArumugamM.S.RaoM.“On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (rms) variants for computing optimal control of a class of hybrid systems”Journal of Applied Soft Computing2008832433610.1016/j.asoc.2007.01.010 – reference: Zhiming, L., Cheng, W. and Jian, L., “Solving constrained optimization via a modified genetic particle swarm optimization,” in Workshop on Knowledge Discovery and Data Mining, WKDD'08, IEEE Computer Society, pp. 217–220, 2008. – reference: Vanneschi, L., “Theory and Practice for Efficient Genetic Programming,” Ph.D. thesis, Faculty of Sciences, University of Lausanne, Switzerland, 2004. – reference: Kennedy, J. and Eberhart, R. C., Swarm Intelligence, Morgan Kaufmann Publishers, 2001. – reference: Vanneschi, L., Codecasa, D. and Mauri, G., “A study of parallel and distributed particle swarm optimization methods,” in Proc. of the 2nd workshop on Bio-inspired algorithms for distributed systems, BADS'10, New York, NY, USA, ACM, pp. 9–16, 2010. – reference: You, X., Liu, S. and Zheng, W., “Double-particle swarm optimization with induction enhanced evolutionary strategy to solve constrained optimization problems,” in IEEE International Conference on Natural Computing, ICNC'07, IEEE Computer Society, pp. 527–531, 2007. – reference: Bonabeau, E., Dorigo, M. and Theraulaz, G., Swarm Intelligence: From Natural to Artificial Systems (Santa Fe Institute Studies in the Sciences of Complexity), Oxford University Press, New York, NY, 1999. – reference: DiosanL.OlteanM.“What else is evolution of pso telling us?”Journal of Artificial Evolution and Applications20081511210.1155/2008/289564 – reference: NiuB.ZhuY.HeX.WuH.“MCPSO: A multi-swarm cooperative particle swarm optimizer”Applied Mathematics and Computation200721851050106210.1016/j.amc.2006.07.026 – reference: Smola, A. J. and Scholkopf, B., “A Tutorial on Support Vector Regression,” Technical Report Technical Report Series - NC2-TR-1998-030, NeuroCOLT2, 1999. – reference: Liang, J. J. and Suganthan, P. N., “Dynamic multi-swarm particle swarm optimizer with local search,” in 2005 IEEE Congress on Evolutionary Computation, CEC 2005, 1, pp. 522–528, 2005. – reference: Wu, Z. and Zhou, J., “A self-adaptive particle swarm optimization algorithm with individual coefficients adjustment,” in Proc. IEEE International Conference on Computational Intelligence and Security, CIS'07, IEEE Computer Society, pp. 133–136, 2007. – reference: ValleY.D.VenayagamoorthyG.MohagheghiS.HernandezJ.HarleyR.“Particle swarm optimization: Basic concepts, variants and applications in power systems”IEEE Transactions on Evolutionary Computation200812217119510.1109/TEVC.2007.896686 – reference: Riget, J. and Vesterstrm, J., “A diversity-guided particle swarm optimizer - the arpso,” Technical report, Dept. of Comput. Sci., Aarhus Univ., Denmark, 2002. – reference: Suganthan, P., Hansen, N., Liang, J., Deb, K., Chen, Y., Auger, A. and Tiwari, S., “Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization,” Technical Report Number 2005005, Nanyang Technological University, 2005. – reference: White, T. and Pagurek, B., “Towards multi-swarm problem solving in networks,” in Proc. of Third International Conference on Multi-Agent Systems (ICMAS'98), IEEE Computer Society, pp. 333–340, 1998. – reference: Kwong, H. and Jacob, C., “Evolutionary exploration of dynamic swarm behavior,” in IEEE Congress on Evolutionary Computation, CEC'03, IEEE Press, pp. 367–374, 2003. – reference: Blackwell, T. M., “Swarm music: improvised music with multi-swarms,” in Proc. of the 2003 AISB Symp. on Artificial Intelligence and Creativity in Arts and Science, pp. 41–49, 2003. – reference: Srinivasan, D. and Seow, T. H., “Particle swarm inspired evolutionary algorithm (ps-ea) for multi-objective optimization problem,” in IEEE Congress on Evolutionary Computation, CEC03, IEEE Press, pp. 2292–2297, 2003. – reference: ArchettiF.MessinaE.LanzeniS.VanneschiL.“Genetic programming for computational pharmacokinetics in drug discovery and development”Genetic Programming and Evolvable Machines200784172610.1007/s10710-007-9040-z – reference: N. C. M. Project, National Cancer Institute, Bethesda MD, 2008. See http://genome-www.stanford.edu/nci60/. – reference: RossS.M.Introduction to Probability and Statistics for Engineers and scientists2000NewYorkAcademic Press0942.62001 – reference: Vanneschi, L., Codecasa, D. and Mauri, G., “An empirical comparison of parallel and distributed particle swarm optimization methods,” in Proc. of the Genetic and Evolutionary Computation Conference, GECCO 2010 (Branke, J. et al. eds.), ACM Press, 2010. To appear. – reference: ArchettiF.GiordaniI.VanneschiL.“Genetic programming for QSAR investigation of docking energy”Applied Soft Computing201010117018210.1016/j.asoc.2009.06.013 – reference: Zhigljavsky, A. and Zilinskas, A., “Stochastic Global Optimization,” Springer Optimization and Its Applications, 9, Springer, 2008. – reference: Kennedy, J. and Mendes, R., “Population structure and particle swarm performance,” in IEEE Congress on Evolutionary Computation, CEC'02, IEEE Computer Society, pp. 1671–1676, 2002. – reference: Kennedy, J. and Eberhart, R., “Particle swarm optimization,” in Proc. IEEE Int. conf. on Neural Networks, 4, IEEE Computer Society, pp. 1942–1948, 1995. – reference: KennedyJ.PoliR.BlackwellT.“Particle swarm optimization: an overview”Swarm Intelligence200711335710.1007/s11721-007-0002-0 – reference: Poli, R., “Analysis of the publications on the applications of particle swarm optimization,” Journal of Artificial Evolution and Applications, 2009, (in press). – reference: Diosan, L. and Oltean, M., “Evolving the structure of the particle swarm optimization algorithms,” in EvoCOP'06, Springer Verlag, pp. 25–36, 2006. – reference: Kameyama, K., “Particle swarm optimization - a survey,” IEICE Transactions, 92-D, 7, pp. 1354–1361, 2009. – reference: Lu, F.-Q., Huang, M., Ching, W.-K., Wang, X.-W. and Sun, X.-l., “Multi-swarm particle swarm optimization based risk management model for virtual enterprise,” in GEC '09: Proc. of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, New York, NY, USA, ACM, pp. 387–392, 2009. – reference: Blackwell, T. and Branke, J., “Multi-swarm optimization in dynamic environments,” in EvoWorkshops (Raidl, G. 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Snippet	We present four new parallel and distributed particle swarm optimization methods consisting in a genetic algorithm whose individuals are co-evolving swarms, an...
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SubjectTerms	Artificial Intelligence Computer Hardware Computer Science Computer Systems Organization and Communication Networks Software Engineering/Programming and Operating Systems
Title	A Comparative Study of Four Parallel and Distributed PSO Methods
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