Adapting Differential Evolution Algorithms For Continuous Optimization Via Greedy Adjustment Of Control Parameters

[1] N. Xiong, D. Molina, M. Leon, and F. Herrera, A walk into metaheuristics for engineering optimization: Principles, methods, and recent trends, International Journal of Computational Intelligence Systems, vol. 8, no. 4, pp. 606-636, 2015.10.1080/18756891.2015.1046324Search in Google Scholar

[2] N. Hansen and A. Ostermeier, Completely derandomized self-adaptation in evolution strategies, Evolutionary Computation, vol. 9, no. 2, pp. 159-195, 2001.10.1162/10636560175019039811382355Search in Google Scholar

[3] F. Herrera and M. Lozano, Two-loop real-coded genetic algorithms with adaptive control of mutation step size, Applied Intelligence, vol. 13, pp. 187-204, 2000.10.1023/A:1026531008287Search in Google Scholar

[4] D. Molina, M. Lozano, A. M. Sanchez, and F. Herrera, Memetic algorithms based on local search chains for large scale continuous optimization problems: Ma-ssw-chains, Soft Computing, vol. 15, pp. 2201-2220, 2011.Search in Google Scholar

[5] R. Storn and K. Price, Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, vol. 11, no. 4, pp. 341 - 359, 1997.10.1023/A:1008202821328Search in Google Scholar

[6] J. Kenedy and R. C. Eberhart, Particle swarm optimization, in In Proc. IEEE Conference on Neural Networks, 1995, pp. 1942-1948.Search in Google Scholar

[7] D. Karaboga, B. Gorkemli, C.Ozturk, and N. Karaboga, A comprehensive survey: artificial bee colony (abc) algorithm and applications, Artificial Intelligence Review, vol. 42, no. 1, pp. 21-57, 2012.10.1007/s10462-012-9328-0Search in Google Scholar

[8] M. Ali and A. Torn, Population set based global optimization algorithms: Some modifications and numerical studies, Computers and Operations Research, vol. 31, pp. 1703-1725, 2004.Search in Google Scholar

[9] S. Garcia, D. Molina, M. Lozano, and F. Herrera, A study on the use of non-parametric tests for analyzing the evolutionary algorithmss behaviour: A case study on the cec2005special session on real parameter optimization, Journal of Heuristics, vol. 15, no. 6, pp. 617-644, 2009.10.1007/s10732-008-9080-4Search in Google Scholar

[10] S. Das and N. Suganthan, Differential evolution: A survey of the state-of-the-art, in IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, 2011, pp. 4-31.10.1109/TEVC.2010.2059031Search in Google Scholar

[11] R. Gamperle, S. D. Muller, and P. Koumoutsakos, A parameter study for differential evolution, in Advances in intelligent systems, fuzzy systems, evolutionary computation, vol. 10, 2002, pp. 293-298.Search in Google Scholar

[12] K. Zielinski, P. Weitkemper, R. Laur, and K. D. Kammeyer, Parameter study for differential evolution using a power allocation problem including interference cancellation, in IEEE Congress on Evolutionary Computation, 2006, pp. 1857-1864.Search in Google Scholar

[13] J. Zhang and A. C. Sanderson, An approximate gaussian model of differential evolution with spherical fitness functions, in Proc. IEEE Congress on Evolutionary Computation, 2007, pp. 2220-2228.Search in Google Scholar

[14] J. Liu and J. Lampinen, A fuzzy adaptive differential evolution algorithm, Soft Computing, vol. 9, no. 6, pp. 448-462, 2005.10.1007/s00500-004-0363-xSearch in Google Scholar

[15] F. Xue, A. C. Sanderson, P. P. Bonissone, and R. J. Graves, Fuzzy logic controlled multiobjective differential evolution, in Proc. IEEE Conference on Fuzzy Systems, 2005, pp. 720-725.Search in Google Scholar

[16] A. Qin and P. Suganthan, Self-adaptive differential evolution algorithm for numerical optimization, The 2005 IEEE Congress on Evolutionary Computation, vol. 2, pp. 1785-1791, 2005.Search in Google Scholar

[17] J. Zhang and A. Sanderson, Jade: Adaptive differential evolution with optional external archive, IEEE Transactions on Evolutionary Computation, vol. 13, pp. 945-958, 2009.10.1109/TEVC.2009.2014613Search in Google Scholar

[18] S. M. Islam, S. Das, S. Ghoshand, S. Roy, and P. N. Suganthan, An adaptive differential evolution algorithm with novel mutation and crossover strategies for global numerical optimization, Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, vol. 42, no. 2, pp. 482-500, 2012.10.1109/TSMCB.2011.216796622010153Search in Google Scholar

[19] Z. Yang, K. Tang, and X. Yao, Scability of generalized adaptive differential evolution for large-scale continuous optimization, Soft Computing, vol. 15, no. 11, pp. 2141-2155, 2001.Search in Google Scholar

[20] R. Tanabe and A. Fukinga, Success-history based parameter adaptation for differential evolution, in 2013 IEEE Congress on Evolutionary Computation (CEC), Cancun, Mexico, 2013, pp. 71-78.10.1109/CEC.2013.6557555Search in Google Scholar

[21] M. Leon and N. Xiong, Investigation of mutation strategies in differential evolution for solving global optimization problems, in Artificial Intelligence and Soft Computing. springer, June 2014, pp. 372-383.10.1007/978-3-319-07173-2_32Search in Google Scholar

[22] X. Yao, Y. Liu, and G. Lin, Evolutionary programming made faster, in Proc. IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, 1999, pp. 82-102.10.1109/4235.771163Search in Google Scholar

[23] P. N. Suganthan, N. Hansen, J. J. Liang, K. Deb, Y. P. Chen, A. Auger, and S. Tiwari, Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization, Technical Report, Nanyang Technological University, Singapore And KanGAL Report Number 2005005 (Kanpur Genetic Algorithms Laboratory, IIT Kanpur), Tech. Rep., May 2005.Search in Google Scholar

[24] D. Wolpert and W. Macready, No free lunch theorems for optimization, IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, pp. 67-82, 1997.10.1109/4235.585893Search in Google Scholar

[25] D. Whitley and J. Rowe, Focused no free lunch theorems, in Proc. Conf. Genetic Evolutionary Computing, 2008, pp. 811-818.10.1145/1389095.1389254Search in Google Scholar

[26] M. Leon and N. Xiong, Using random local search helps in avoiding local optimum in diefferential evolution, in Proc. Artificial Intelligence and Applications, AIA2014, Innsbruck, Austria, 2014, pp. 413-420. Search in Google Scholar