Abstract
We propose a novel generalized algorithmic framework to utilize particle filter for optimization incorporated with the swarm move method in particle swarm optimization (PSO). In this way, the PSO update equation is treated as the system dynamic in the state space model, while the objective function in optimization problem is designed as the observation/measurement in the state space model. Particle filter method is then applied to track the dynamic movement of the particle swarm and therefore results in a novel stochastic optimization tool, where the ability of PSO in searching the optimal position can be embedded into the particle filter optimization method. Finally, simulation results show that the proposed novel approach has significant improvement in both convergence speed and final fitness in comparison with the PSO algorithm over a set of standard benchmark problems.
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References
Doucet, A., De Freitas, J., Gordon, N. (eds.): Sequential Monte Carlo Methods in Practice. Springer, Heidelberg (2001)
Zhang, Y., Ji, C., Walik, W.Q., Liu, Y., O’Brien, D.C., Edwards, D.J.: Joint Antenna and User Selection Algorithm for Uplink of Multiuser MIMO Systems Using the Sequential Monte Carlo Optimization. In: IEEE Workshop on Statistical Signal Processing, SSP 2007, Madison, Wisconsin, pp. 493–496. IEEE Press, Los Alamitos (2007)
Shi, Y., Eberhart, R.: Empirical Study of Particle Swarm Optimization. In: IEEE Congress on Evolutionary Computation, CEC 1999, pp. 1945–1950. IEEE Press, Piscataway (1999)
Riget, J., Vesterstroem, J.S.: A diversity-guided Particle Swarm Optimizer - the ARPSO. Department of Computer Science, University of Aarhus, Technique Report (2002)
Angeline, P.J.: Using Selection to Improve Particle Swarm Optimization. In: IEEE Congress on Evolutionary Computation, CEC 1998, Anchorage, Alaska, USA, pp. 84–89. IEEE Press, Los Alamitos (1998)
Clerc, M., Kennedy, J.: The particle Swarm-explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)
Monson, C.K., Seppi, K.D.: The Kalman Swarm. In: Deb, K., et al. (eds.) GECCO 2004. LNCS, vol. 3102, pp. 140–150. Springer, Heidelberg (2004)
Gordon, N., Salmond, D., Smith, A.: Novel Approach to Nonlinear/ Non-gaussian Bayesian State Estimation. IEE Proceedings Radar and Signal Processing 140(2), 107–113 (1993)
Harvey, A.C.: Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, Cambridge (1990)
Doucet, A., Godsill, S., Andrieu, C.: On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing 10, 197–208 (2000)
Moral, P.D., Doucet, A., Jasra, A.: Sequential Monte Carlo Samplers. Royal Statistical Society 68, 411–436 (2006)
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Ji, C., Zhang, Y., Tong, M., Yang, S. (2008). Particle Filter with Swarm Move for Optimization. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds) Parallel Problem Solving from Nature – PPSN X. PPSN 2008. Lecture Notes in Computer Science, vol 5199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87700-4_90
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DOI: https://doi.org/10.1007/978-3-540-87700-4_90
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