Journal of Computing Science and Engineering

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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An Optimization Algorithm with Novel Flexible Grid: Applications to Parameter Decision in LS-SVM

  • Gao, Weishang (Information and Engineering College, Dalian University) ;
  • Shao, Cheng (Institute of Advanced Control, Dalian University of Technology) ;
  • Gao, Qin (School of Control Science and Engineering, Dalian University of Technology)
  • Received : 2014.08.25
  • Accepted : 2015.03.30
  • Published : 2015.06.30
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Abstract

Genetic algorithm (GA) and particle swarm optimization (PSO) are two excellent approaches to multimodal optimization problems. However, slow convergence or premature convergence readily occurs because of inappropriate and inflexible evolution. In this paper, a novel optimization algorithm with a flexible grid optimization (FGO) is suggested to provide adaptive trade-off between exploration and exploitation according to the specific objective function. Meanwhile, a uniform agents array with adaptive scale is distributed on the gird to speed up the calculation. In addition, a dominance centroid and a fitness center are proposed to efficiently determine the potential guides when the population size varies dynamically. Two types of subregion division strategies are designed to enhance evolutionary diversity and convergence, respectively. By examining the performance on four benchmark functions, FGO is found to be competitive with or even superior to several other popular algorithms in terms of both effectiveness and efficiency, tending to reach the global optimum earlier. Moreover, FGO is evaluated by applying it to a parameter decision in a least squares support vector machine (LS-SVM) to verify its practical competence.

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References

  1. A. W. Mohamed and H. Z. Sabry, "Constrained optimization based on modified differential evolution algorithm," Information Sciences, vol. 194, pp. 171-208, 2012. https://doi.org/10.1016/j.ins.2012.01.008
  2. M. Pelikan, D. E. Goldberg, and F. G. Lobo, "A survey of optimization by building and using probabilistic models," Computational Optimization and Applications, vol. 21, no. 1, pp. 5-20, 2002. https://doi.org/10.1023/A:1013500812258
  3. C. von Lcken, B. Barn, and C. Brizuela, "A survey on multi-objective evolutionary algorithms for many-objective problems," Computational Optimization and Applications, vol. 53, no. 3, pp. 707-756, 2014.
  4. D. V. Arnold and H. G. Beyer, "A comparison of evolution strategies with other direct search methods in the presence of noise," Computational Optimization and Applications, vol. 24, no. 1, pp. 135-159, 2003. https://doi.org/10.1023/A:1021810301763
  5. S. Saha and S. Bandyopadhyay, "A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters," Information Sciences, vol. 179, no. 19, pp. 3230-3246, 2009. https://doi.org/10.1016/j.ins.2009.06.013
  6. Y. Tominaga, Y. Okamoto, S. Wakao, and S. Sato, "Binarybased topology optimization of magnetostatic shielding by a hybrid evolutionary algorithm combining genetic algorithm and extended compact genetic algorithm," IEEE Transactions on Magnetics, vol. 49, no. 5, pp. 2093-2096, 2013. https://doi.org/10.1109/TMAG.2013.2240282
  7. K. Deb and S. Srivastava, "A genetic algorithm based augmented Lagrangian method for constrained optimization," Computational Optimization and Applications, vol. 53, no. 3, pp. 869-902, 2012. https://doi.org/10.1007/s10589-012-9468-9
  8. C. C. Lin, "Dynamic router node placement in wireless mesh networks: a PSO approach with constriction coefficient and its convergence analysis," Information Sciences, vol. 232, p. 294-308, 2013. https://doi.org/10.1016/j.ins.2012.12.023
  9. J. Fernandez-Martinez and E. Garcia-Gonzalo, "Stochastic stability analysis of the linear continuous and discrete PSO models," IEEE Transactions on Evolutionary Computation, vol. 15, no. 3, pp. 405-423, 2011. https://doi.org/10.1109/TEVC.2010.2053935
  10. H. Mabed, A. Caminada, and J. K. Hao, "Genetic tabu search for robust fixed channel assignment under dynamic traffic data," Computational Optimization and Applications, vol. 50, no. 3, pp. 483-506, 2011. https://doi.org/10.1007/s10589-010-9376-9
  11. B. A. Sawyerr, M. M. Ali, and A. O. Adewumi, "A comparative study of some real-coded genetic algorithms for unconstrained global optimization," Optimization Methods and Software, vol. 26, no. 6, pp. 945-970, 2011. https://doi.org/10.1080/10556788.2010.491865
  12. M. K. Dhadwal, S. N. Jung, and C. J. Kim, "Advanced particle swarm assisted genetic algorithm for constrained optimization problems," Computational Optimization and Applications, vol. 58, no. 3, pp. 781-806, 2014. https://doi.org/10.1007/s10589-014-9637-0
  13. Y. J. Wang, "Improving particle swarm optimization performance with local search for high-dimensional function optimization," Optimization Methods and Software, vol. 25, no. 5, pp. 781-795, 2010. https://doi.org/10.1080/10556780903034514
  14. C. Luo, S. L. Zhang, and B. Yu, "Some modifications of low-dimensional simplex evolution and their convergence," Optimization Methods and Software, vol. 28, no. 1, pp. 54-81, 2013. https://doi.org/10.1080/10556788.2011.584876
  15. T. Aittokoski and K. Miettinen, "Efficient evolutionary approach to approximate the Pareto-optimal set in multiobjective optimization, UPS-EMOA," Optimization Methods and Software, vol. 25, no. 6, pp. 841-858, 2010. https://doi.org/10.1080/10556780903548265
  16. M. H. Lim, Y. Yuan, and S. Omatu, "Efficient genetic algorithms using simple genes exchange local search policy for the quadratic assignment problem," Computational Optimization and Applications, vol. 15, no. 3, pp. 249-268, 2000. https://doi.org/10.1023/A:1008743718053
  17. A. El Dor, M. Clerc, and P. Siarry, "A multi-swarm PSO using charged particles in a partitioned search space for continuous optimization," Computational Optimization and Applications, vol. 53, no. 1, pp. 271-295, 2012. https://doi.org/10.1007/s10589-011-9449-4
  18. Y. Tang, Z. Wang, and J. A. Fang, "Controller design for synchronization of an array of delayed neural networks using a controllable probabilistic PSO," Information Sciences, vol. 181, no. 20, pp. 4715-4732, 2011. https://doi.org/10.1016/j.ins.2010.09.025
  19. S. Y. Yuen and C. K. Chow, "A genetic algorithm that adaptively mutates and never revisits," IEEE Transactions on Evolutionary Computation, vol. 13, no. 2, pp. 454-472, 2009. https://doi.org/10.1109/TEVC.2008.2003008
  20. J. Sadeghi, S. Sadeghi, and S. T. A. Niaki, "Optimizing a hybrid vendor-managed inventory and transportation problem with fuzzy demand: an improved particle swarm optimization algorithm," Information Sciences, vol. 272, pp. 126-144, 2014. https://doi.org/10.1016/j.ins.2014.02.075
  21. S. Wang and J. Watada, "A hybrid modified PSO approach to VaR-based facility location problems with variable capacity in fuzzy random uncertainty," Information Sciences, vol. 192, pp. 3-18, 2012. https://doi.org/10.1016/j.ins.2010.02.014
  22. C. W. Ahn, J. An, and J. C. Yoo, "Estimation of particle swarm distribution algorithms combining the benefits of PSO and EDAs," Information Sciences, vol. 192, pp. 109-119, 2012. https://doi.org/10.1016/j.ins.2010.07.014
  23. W. P. Lee and Y. T. Hsiao, "Inferring gene regulatory networks using a hybrid GA-PSO approach with numerical constraints and network decomposition," Information Sciences, vol. 188, pp. 80-99, 2012. https://doi.org/10.1016/j.ins.2011.11.020
  24. Y. Hung and W. Wang, "Accelerating parallel particle swarm optimization via GPU," Optimization Methods and Software, vol. 27, no. 1, pp. 33-51, 2012. https://doi.org/10.1080/10556788.2010.509435
  25. L. N. Xing, Y. W. Chen, and K. W. Yang, "Multi-population interactive coevolutionary algorithm for flexible job shop scheduling problems," Computational Optimization and Applications, vol. 48, no. 1, pp. 139-155, 2011. https://doi.org/10.1007/s10589-009-9244-7
  26. Y. Yang and X. Yu, "Cooperative coevolutionary genetic algorithm for digital IIR filter design," IEEE Transactions on Industrial Electronics, vol. 54, no. 3, pp. 1311-1318, 2007. https://doi.org/10.1109/TIE.2007.893063
  27. M. Baz, B. Hunsaker, and O. Prokopyev, "How much do we pay for using default parameters?," Computational Optimization and Applications, vol. 48, no. 1, pp. 91-108, 2011. https://doi.org/10.1007/s10589-009-9238-5
  28. A. Cassioli, M. Locatelli, and F. Schoen, "Dissimilarity measures for population-based global optimization algorithms," Computational Optimization and Applications, vol. 45, no. 2, pp. 257-281, 2010. https://doi.org/10.1007/s10589-008-9194-5
  29. W. S. Gao, C. Shao, and Q. Gao, "Pseudo-collision in swarm optimization algorithm and solution-rain forest algorithm," Acta Physica Sinica, vol. 62, no. 19, article id. 190202, 2013.
  30. W. Gao, C. Shao, and Y. An, "Bidirectional dynamic diversity evolutionary algorithm for constrained optimization," Mathematical Problems in Engineering, vol. 2013, article id. 762372, 2013.
  31. W. W. Hager and H. Zhang, "Self-adaptive inexact proximal point methods," Computational Optimization and Applications, vol. 39, no. 2, pp. 161-181, 2008. https://doi.org/10.1007/s10589-007-9067-3
  32. M. Al-Baali and H. Khalfan, "A combined class of selfscaling and modified quasi-newton methods," Computational Optimization and Applications, vol. 52, no. 2, pp. 393-408, 2012. https://doi.org/10.1007/s10589-011-9415-1
  33. C. Audet, J. E. Dennis Jr, and S. Le Digabel, "Globalization strategies for mesh adaptive direct search," Computational Optimization and Applications, vol. 46, no. 2, pp. 193-215, 2010. https://doi.org/10.1007/s10589-009-9266-1
  34. A. Nahapetyan and P. Pardalos, "Adaptive dynamic cost updating procedure for solving fixed charge network flow problems," Computational Optimization and Applications, vol. 39, no. 1, pp. 37-50, 2008. https://doi.org/10.1007/s10589-007-9060-x