Abstract
A proposal for particles’ initialization in PSO is presented and discussed, with focus on costly global unconstrained optimization problems. The standard PSO iteration is reformulated such that the trajectories of the particles are studied in an extended space, combining particles’ position and speed. To the aim of exploring effectively and efficiently the optimization search space since the early iterations, the particles are initialized using sets of orthogonal vectors in the extended space (orthogonal initialization, ORTHOinit). Theoretical derivation and application to a simulation-based optimization problem in ship design are presented, showing the potential benefits of the current approach.
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References
Serani, A., Diez, M., Leotardi, C., Peri, D., Fasano, G., Iemma, U., Campana, E.F.: On the use of synchronous and asynchronous single-objective deterministic Particle Swarm Optimization in ship design problems. In: Proceeding of OPT-i, International Conference on Engineering and Applied Sciences Optimization, Kos Island, Greece, June 4-6 (2014)
Clerc, M., Kennedy, J.: The Particle Swarm - Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation 6(1) (2002)
Ozcan, E., Mohan, C.K.: Particle Swarm Optimization: Surfing the Waves. In: Proceedings of the 1999 IEEE Congress on Evolutionary Comnputation, pp. 1939–1944. IEEE Service Center, Piscataway (1999)
Trelea, I.C.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters 85, 317–325 (2003)
Van den Berg, F., Engelbrecht, F.: A Study of Particle Swarm Optimization Particle Trajectories. Information Sciences Journal (2005)
Poli, R.: The Sampling Distribution of Particle Swarm Optimisers and their Stability, Technical Report CSM-465, University of Essex (2007)
Campana, E.F., Fasano, G., Pinto, A.: Dynamic analysis for the selection of parameters and initial population, in particle swarm optimization. Journal of Global Optimization 48, 347–397 (2010)
Campana, E.F., Diez, M., Fasano, G., Peri, D.: Initial particles position and parameters selection for PSO. In: Tan, Y., Shi, Y., Mo, H. (eds.) ICSI 2013, Part I. LNCS, vol. 7928, pp. 112–119. Springer, Heidelberg (2013)
Mendes, R., Kennedy, J., Neves, J.: The fully informed particle swarm: Simpler, maybe better. IEEE Transactions on Evolutionary Computation 8, 204–210 (2004)
Hestenes, M.R., Stiefel, E.: Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards 49, 409–435 (1952)
Fasano, G., Roma, M.: Iterative Computation of Negative Curvature Directions in Large Scale Optimization. Computational Optimization and Applications 38, 81–104 (2007)
Campana, E.F., Liuzzi, G., Lucidi, S., Peri, D., Piccialli, V., Pinto, A.: New Global Optimization Methods for Ship Design Problems. Optimization and Engineering 10, 533–555 (2009)
Chen, X., Diez, M., Kandasamy, M., Zhang, Z., Campana, E.F., Stern, F.: High-fidelity global optimization of shape design by dimensionality reduction, metamodels and deterministic particle swarm. Engineering Optimization (in press, 2014), doi: 10.1080/0305215X.2014.895340
Volpi, S., Diez, M., Gaul, N.J., Song, H., Iemma, U., Choi, K.K., Campana, E.F., Stern, F.: Development and validation of a dynamic metamodel based on stochastic radial basis functions and uncertainty quantification. Structural Multidisciplinary Optimization (in press, 2014), doi: 10.1007/s00158-014-1128-5
Diez, M., He, W., Campana, E.F., Stern, F.: Uncertainty quantification of Delft catamaran resistance, sinkage and trim for variable Froude number and geometry using metamodels, quadrature and Karhunen-Loève expansion. Journal of Marine Science and Technology 19(2), 143–169 (2014), doi:10.1007/s00773-013-0235-0
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Diez, M. et al. (2014). A Proposal of PSO Particles’ Initialization for Costly Unconstrained Optimization Problems: ORTHOinit. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_14
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DOI: https://doi.org/10.1007/978-3-319-11857-4_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11856-7
Online ISBN: 978-3-319-11857-4
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