A region-based quantum evolutionary algorithm (RQEA) for global numerical optimization

Journal of Computational and Applied Mathematics - Tập 239 - Trang 1-11 - 2013
Tzyy-Chyang Lu1,2, Jyh-Ching Juang1
1Department of Electrical Engineering/Green Energy Electronics Research Center, National Cheng Kung University, 1 University Rd., Tainan, Taiwan
2Advanced Institute of Manufacturing with High-tech Innovations, National Chung Cheng University, 168 University Rd., Ming-Hsiung, Chia-Yi, Taiwan

Tài liệu tham khảo

Fogel, 1994, An introduction to simulated evolutionary optimization, IEEE Trans. Neural Netw., 5, 3, 10.1109/72.265956 Yao, 1999, Evolutionary programming made faster, IEEE Trans. Evol. Comput., 3, 82, 10.1109/4235.771163 Bäck, 1996 Yao, 1997, Fast evolution strategies, 151 J. Renders, H. Bersini, Hybridizing genetic algorithms with hill-climbing methods for global optimization: two possible ways, in: Proc. 1st IEEE Conf. Evolutionary Computation, Orlando, FL, 1994, pp. 312–317. Tsai, 2004, Hybrid Taguchi-genetic algorithm for global numerical optimization, IEEE Trans. Evol. Comput., 8, 365, 10.1109/TEVC.2004.826895 J. Kennedy, R.C. Eberhart, Particle swarm optimization, in: Proc. IEEE Int. Conf. Neural Netw., Dec. 1995, pp. 1942–1948. A.K. Qin, P.N. Suganthan, Self-adaptive differential evolution algorithm for numerical optimization, in: Proc. of the 2005 Congr. on Evol. Comput., vol. 2, 2005, pp. 1785–1791. J. Rönkkönen, S. Kukkonen, K. Price, Real-parameter optimization with differential evolution, in: Proc. of the 2005 Congr. on Evol. Comput., vol. 1, 2005, pp. 567–574. Larrañaga, 2001 T.K. Paul, H. Iba, Real-coded estimation of distribution algorithm, in: Proc. of the 5th Metaheuristics Int. Conf., 2003. S. Yang, M. Wang, L. Jiao, A novel quantum evolutionary algorithm and its application, in: Proc. of the 2004 Congr. on Evol. Comput., Jun. 2004, pp. 820–826. Neri, 2009, Scale factor local search in differential evolution, Memetic Comput. J., 1, 153, 10.1007/s12293-009-0008-9 S. Kimura, A. Konagaya, High dimensional function optimization using a new genetic local search suitable for parallel computers, in: Proc. IEEE Int. Conf. Syst., Man, and Cybern., vol. 1, Oct. 2003, pp. 335–342. D. Molina, M. Lozano, F. Herrera, Memetic algorithm with local search chaining for large scale continuous optimization problems, in: Proc. of the 2009 Congr. on Evol. Comput., 2009, pp. 830–837. Krasnogor, 2005, A tutorial for competent memetic algorithms: model, taxonomy, and design issue, IEEE Trans. Evol. Comput., 9, 474, 10.1109/TEVC.2005.850260 Noman, 2008, Accelerating differential evolution using an adaptive local search, IEEE Trans. Evol. Comput., 12, 107, 10.1109/TEVC.2007.895272 Tirronen, 2008, An enhanced memetic differential evolution in filter design for defect detection in paper production, Evol. Comput. J., 16, 529, 10.1162/evco.2008.16.4.529 Liu, 2007, An effective PSO-based memetic algorithm for flow shop scheduling, IEEE Trans. Syst. Man Cybern., 37, 18, 10.1109/TSMCB.2006.883272 Y.X. Wang, Z.D. Zhao, R. Ren, Hybrid particle swarm optimizer with tabu strategy for global numerical optimization, in: Proc. of the 2007 Congr. on Evol. Comput., 2007, pp. 2310–2316. S. Zhao, J.J. Liang, P.N. Suganthan, M.F. Tasgetiren, Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization, in: Proc. of the 2008 Congr. on Evol. Comput., 2008, pp. 3845–3852. H.K. Birru, K. Chellapilla, S.S. Rao, Local search operators in fast evolutionary programming, in: Proc. of the 1999 Congr. on Evol. Comput., vol. 2, Jul. 1999, pp. 1506–1513. Claudio, 2003, Parallel island genetic algorithm applied to a nuclear power plant auxiliary feed water system surveillance tests policy optimization, Ann. Nucl. Energy, 1665 Z.Y. Zhu, K.S. Leung, Asynchronous self-adjustable island genetic algorithm for multi-objective optimization problems, in: Proc. of the 2002 Congr. on Evol. Comput., May. 2002, pp. 837–842. J. Sarma, K.D. Jong, An analysis of local selection algorithms in a spatially structured evolutionary algorithm, in: Proc. 7th Int. Conf. Genetic Algorithms, 1997, pp. 181–186. Lim, 2007, Efficient hierarchical parallel genetic algorithms using grid computing, Future Gener. Comput. Syst., 23, 658, 10.1016/j.future.2006.10.008 J. Herrera, E. Huedo, R.S. Montero, I.M. Llorente, A grid-oriented genetic algorithm, in: 3rd European Grid Conference, vol. 3470, 2005, pp. 315–322. T. Nakashima, T. Ariyama, T. Yoshida, H. Ishibuchi, Performance evaluation of combined cellular genetic algorithms for function optimization problems, in: Proc. of 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation, July. 2003, pp. 295–299. G. Dick, The spatially-dispersed genetic algorithm: an explicit spatial population structure for GAs, in: Proc. of the 2003 Congr. on Evol. Comput., 2003, pp. 2455–2461. Zhong, 2004, A multiagent genetic algorithm for global numerical optimization, IEEE Trans. Syst. Man Cybern., 34, 1128, 10.1109/TSMCB.2003.821456 Y. Zhao, Y. Chen, M. Pan, Q. Zhu, A region reproduction algorithm for global numerical optimization, in: Proc. of the 2008 Congr. on Evol. Comput., Jun. 2008, pp. 3601–3605. Tu, 2004, A robust stochastic genetic algorithm (StGA) for global numerical optimization, IEEE Trans. Evol. Comput., 8, 456, 10.1109/TEVC.2004.831258 Leung, 2001, An orthogonal genetic algorithm with quantization for global numerical optimization, IEEE Trans. Evol. Comput., 5, 41, 10.1109/4235.910464 D. Jiang, Z. Wu, J. Zou, M. Wei, L. Kang, Algorithm based on heuristic subspace searching strategy for solving investment portfolio optimization problems, in: Proc. of the 2008 Congr. on Evol. Comput., Jun. 2008, pp. 607–611. Sun, 2005, DE/EDE: a new evolutionary algorithm for global optimization, Inf. Sci., 169, 249, 10.1016/j.ins.2004.06.009 Han, 2002, Quantum-inspired evolutionary algorithm for a class of combinatorial optimization, IEEE Trans. Evol. Comput., 6, 580, 10.1109/TEVC.2002.804320 K.H. Han, J.H. Kim, On the analysis of the quantum-inspired evolutionary algorithm with a single individual, in: Proc. of the 2006 Congr. on Evol. Comput., Jul. 2006, pp. 2622–2629. R. Zhang, H. Gao, Improved quantum evolutionary algorithm for combinational optimization problem, in: Proc. of the Sixth International Conference on Machine Learning and Cybernetics, 2007, pp. 3501–3505. Han, 2004, Quantum-inspired evolutionary algorithms with a new termination criterion, H∈ gate, and two-phase scheme, IEEE Trans. Evol. Comput., 8, 156, 10.1109/TEVC.2004.823467 Jiao, 2008, Quantum-inspired immune colonial algorithm for global optimization, IEEE Trans. Syst. Man Cybern., 35, 1234, 10.1109/TSMCB.2008.927271 H. Liu, G. Zhang, C. Liu, C. Fang, A novel memetic algorithm based on real-observation quantum-inspired evolutionary algorithms, in: Proc. of the 2008 Conf. on Intell. Syst. and Knowl. Eng., ISKE 2008, vol. 1, Nov. 2008, pp. 486–490. Shang, 2006, A note on the extended Rosenbrock function, Evol. Comput. J., 14, 119, 10.1162/evco.2006.14.1.119