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A new multi-particle collision algorithm for optimization in a high performance environment

doi: 10.6062/jcis.2008.01.01.0001(Free PDF)

Authors

Eduardo Fávero Pacheco da Luz, José Carlos Becceneri and Haroldo Fraga de Campos Velho

Abstract

A new meta-heuristics is introduced here: the Multi-Particle Collision Algorithm (M-PCA). The M-PCA is based on the implementation of a function optimization lgorithm driven for a collision process of multiple particles. A parallel version for the M-PCA is also described. The complexity for PCA, M-PCA, and a parallel mplementation for the MPCA is developed. The efficiency for optimization for PCA and M-PCA is evaluated for some test functions. The performance of the parallel mplementation of the M-PCA is also presented. The results with M-PCA produced better optimized solutions for all test functions analyzed.

Keywords

Computational mathematics, computational complexity, high performance computing, meta-heuristics, optimization.

References

[1] ANTONIOU A & LU W-S. 2007. Practical optimization: algorithms and engineering applications. Springer, New York.

[2] BECCENERI JC. 2008. Chapter Meta-Heurísticas e Otimização Combinatória: Aplicações em Problemas Ambientais. INPE, São José dos Campos.

[3] DORIGO M & STUTZLE T. 2004. Ant Colony Optimization. The MIT Press, Cambridge.

[4] FLYNN MJ. 1966. High-speed computing systems. Proceedings of the IEEE, 54(12): 1901-1909. doi: 10.1109/PROC.1966.5273

[5] GOLDBERG DE. 1989. Genetic Algorithms in search optimization and Machine Learning. Addison-Wesley, Boston, MA, USA.

[6] HOLLAND JH. 1992. Adaptation in natural and artificial systems. MIT Press, Cambridge, MA, USA.

[7] KASIBHATLA P, HEIMANN M, RAYNER P, MAHOWALD N, PRINN RG & HARTLEY DE (Eds.). 2000. Inverse Methods in Global Biogeochemical Cycles. American Geophysical Union, Washington, USA.

[8] KENNEDY J & EBERHART EC. 1995. Particle swarm optimization. IEEE Int. Conf. Neural Networks, 4: 1942-1948. doi: 10.1109/LPT.2004.838150

[9] KIRKPATRIK S, GELATT CD & Vecchi MP. 1983. Optimization by simulated annealing. Science, 220: 671-680. doi: 10.1126/science.220.4598.671

[10] LUZ EFP. 2007. Estimação de fonte de poluição atmosférica usando otimizacao por enxame de partículas. Master's thesis, Computacao Aplicada, INPE, São José dos Campos.

[11] LUZ EFP, VELHO HFC, BECCENERI JC & ROBERTI DR. 2007. Estimating atmospheric area source strength through particle swarm optimization. Florida: Proceedings of IPDO.

[12] MEHRABIAN AR & LUCAS C. 2006. A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics 1: 355-366. doi: 10.1016/j.ecoinf.2006.07.003

[13] METROPOLIS N, ROSENBLUTH AW, ROSENBLUTH MN, TELLERAH & TELLER E. 1953. Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21: 1087-1092. doi: 10.1063/1.1699114

[14] PACHECO P. 1996. Parallel programming with MPI. Morgan Kauf-mann Publishers, San Francisco, USA.

[15] ROBERTI DR, ANFOSSI A, VELHO HFC & DEGRAZIA GA. 2005. Estimation of emission rate of pollutant armospheric source. Proceeding of ICIPE 2005, 3: R03-1-R03-8.

[16] SACCO WF & DE OLIVEIRA CRE. 2005. A new stochastic optimiza-tion algorithm based on a particle collision metaheuristic. Proceedings of 6th WCSMO.

[17] SACCO WF, FILHO HA & PEREIRA CMNA. 2007. Cost-based opti-mization of a nuclear reactor core design: a preliminary model. Proceedings of INAC.

[18] SACCO WF, LAPA CMF, PEREIRA CMNA & FILHO HAA. 2006. Two stochastic optimization algorithms applied to nuclear reactor core design. Progress in Nuclear Energy, 525-539. doi: 10.1016/j.pnucene.2005.10.004

[19] SACCO WF, LAPA CMF, PEREIRA CMNA & FILHO HAA. 2008. A metropolis algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization. Progress in Nuclear Energy, 15-21.

[20] SAMBATTI SBM. 2004. Diferentes estratégias de paralelização de um algoritmo genético epidêmico aplicadas na solução de problemas inversos. Master's thesis. Computação Aplicada, INPE, São José dos Campos.

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