Abstract
As is typical of metaheuristic optimization algorithms, particle swarm optimization is guided solely by the objective function. However, experience with separable and roughly separable problems suggests that, for subsets of the decision variables, the use of alternative ‘guide objectives’ may result in improved performance. This paper describes how, through the use of such guide objectives, simple problem domain knowledge may be incorporated into particle swarm optimization and illustrates how such an approach can be applied to both academic optimization problems and a real-world optimization problem from the domain of petroleum engineering.
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References
PUNQ-S3 test case. Department of Earth Science and Engineering. Imperial College, London (2010), http://www3.imperial.ac.uk/earthscienceandengineering/research/perm/punq-s3model (retrieved January 7, 2010)
Abdollahzadeh, A., Reynolds, A., Christie, M., Corne, D., Davies, B., Williams, G.: Bayesian optimization algorithm applied to uncertainty quantification. In: SPE EUROPEC/EAGE Annual Conf. and Exhibition, Vienna, Austria (2011)
Abdollahzadeh, A., Reynolds, A., Christie, M., Corne, D., Williams, G., Davies, B.: Estimation of distribution algorithms applied to history matching. In: SPE Reservoir Simulation Symposium, Houston, Texas, U.S.A. (2011)
Barker, J.W., Cuypers, M., Holden, L.: Quantifying uncertainty in production forecasts: Another look at the PUNQ-S3 problem. SPE Journal 6(4), 433–441 (2001)
Erbaş, D., Christie, M.A.: Effect of sampling strategies on prediction uncertainty estimation. In: SPE Reservoir Simulation Symposium, Houston, U.S.A. (2007)
Hajizadeh, Y., Christie, M., Demyanov, V.: Comparative study of novel population-based optimization algorithms for history matching and uncertainty quantification: PUNQ-S3 revisited. In: Abu Dhabi Int. Petroleum Exhibition and Conf. (2010)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. of the IEEE Int. Conf. on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Kvasnicka, V., Pelikan, M., Pospichal, J.: Hill climbing with learning (an abstraction of genetic algorithm). Neural Network World 6(5), 773–796 (1995)
Mohamed, L., Christie, M.A., Demyanov, V.: Comparison of stochastic sampling algorithms for uncertainty quantification. SPE Journal 15(1), 31–38 (2010)
Mohammed, L., Christie, M., Demyanov, V.: Reservoir model history matching with particle swarms. In: SPE Oil and Gas India Conf. and Exhibition, Mumbai, India (2010)
Petrovska, I., Carter, J.N.: Estimation of distribution algorithms for history-matching. In: Proc. of the 10th European Conf. on the Mathematics of Oil Recovery. EAGE, Amsterdam (2006)
Romero, C.E., Carter, J.N., Gringarten, A.C., Zimmerman, R.W.: A modified genetic algorithm for reservoir characterisation. In: Int. Oil and Gas Conf. and Exhibition, Beijing, China (2000)
Rosenbrock, H.H.: An automatic method for finding the greatest or least value of a function. The Computer Journal 3(3), 175–184 (1960)
Sultan, A.J., Ouenes, A., Weiss, W.W.: Automatic history matching for an integrated reservoir description and improving oil recovery. In: Proc. of the Permian Basin Oil and Gas Recovery Conf., Midland, Texas, pp. 741–748 (1994)
Yang, C., Nghiem, L., Card, C., Bremeier, M.: Reservoir model uncertainty quantification through computer-assisted history matching. In: SPE Annual Technical Conf. and Exhibition, Anaheim, California, U.S.A. (2007)
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Reynolds, A.P., Abdollahzadeh, A., Corne, D.W., Christie, M., Davies, B., Williams, G. (2012). Guide Objective Assisted Particle Swarm Optimization and Its Application to History Matching. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32964-7_20
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DOI: https://doi.org/10.1007/978-3-642-32964-7_20
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