Otwarty dostęp

Effect of Strategy Adaptation on Differential Evolution in Presence and Absence of Parameter Adaptation: An Investigation


Zacytuj

[1] A. E. Eiben, R. Hinterding, Z. Michalewicz, Parameter control in evolutionary algorithms, IEEE Transactions on Evolutionary Computation, 3 (2), 124–141, 1999.10.1109/4235.771166Open DOISearch in Google Scholar

[2] G. Beni, J. Wang, Swarm Intelligence in Cellular Robotic Systems, in: Proceedings of the NATO Advanced Workshop on Robots and Biological Systems. Tuscany, Italy, 1989.Search in Google Scholar

[3] P.J. Angeline, Adaptive and self-adaptive evolutionary computation, in: M. Palaniswami, Y. Attikiouzel, R.J. Marks, D.B. Fogel, T. Fukuda (Eds.), Computational Intelligence: A Dynamic System Perspective, IEEE Press, pp. 152–161, 1995.Search in Google Scholar

[4] J. Gomez, D. Dasgupta, F. Gonazalez, Using adaptive operators in genetic search, in: Proceedings of the Genetic and Evolutionary Computation Conference 2003 (GECCO03), Chicago, Illinois, USA, pp. 1580–1581, 2003.Search in Google Scholar

[5] B. R. Julstrom, What have you done for me lately? Adapting operator probabilities in a steady-state genetic algorithm, in: Proceedings of the 6th International Conference on Genetic Algorithms, Pittsburgh, PA, USA, pp. 81-87, 1995.Search in Google Scholar

[6] J. E. Smith, T.C. Fogarty, Operator and parameter adaptation in genetic algorithms, Soft Computing 1, pp. 81-87, 1997.10.1007/s005000050009Search in Google Scholar

[7] A. Tuson, P. Ross, Adapting operator settings in genetic algorithms, Evolutionary Computation 6, pp. 161-184, 1998.10.1162/evco.1998.6.2.16110021745Open DOISearch in Google Scholar

[8] R. M. Storn, K. V. Price, Differential evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces, International Computer Science Institute, Berkeley, CA, USA, ICSI Technical Report 95-012, 1995.Search in Google Scholar

[9] S. Das, P. N. Suganthan, Differential evolution - A survey of the state-of-the-art, IEEE Transactions on Evolutionary Computation, 15 (1), pp. 4–31, 2011.10.1109/TEVC.2010.2059031Open DOISearch in Google Scholar

[10] R. M. Storn, K. V. Price, Minimizing the real functions of the ICEC 1996 contest by differential evolution, in: Proceedings of IEEE International Conference on Evolutionary Computation, pp. 842–844, 1996.Search in Google Scholar

[11] J. Liu, J. Lampinen, On setting the control parameter of the differential evolution method, in: Proceedings of 8th Int. Conference Soft Computing (MENDEL), pp. 11–18, 2002.Search in Google Scholar

[12] R. Gamperle, S. D. Muller, P. Koumoutsakos, A parameter study for differential evolution, NNAFSFS-EC 2002, Interlaken, Switzerland, WSEAS, pp. 11–15, 2002.Search in Google Scholar

[13] A. E. Eiben, J. E. Smith, Introduction to Evolutionary Computing, Natural Computing. Berlin, Germany: Springer-Verlag, 2003.10.1007/978-3-662-05094-1Search in Google Scholar

[14] K. Price, R. Storn, J. Lampinen, Differential Evolution - A Practical Approach to Global Optimization, Berlin, Germany: Springer, 2005.Search in Google Scholar

[15] S. Das, A. Konar, U. K. Chakraborty, Two improved Differential Evolution schemes for faster global search, in Proceedings of ACM-SIGEVO GECCO, pp. 991–998, 2005.10.1145/1068009.1068177Search in Google Scholar

[16] H. A. Abbass, The self-adaptive pareto differential evolution algorithm, in Proceedings of the 2002 IEEE Congress on Evolutionary Computation, Honolulu, Hawaii, USA, 1, pp. 831–836, 2002.Search in Google Scholar

[17] J. Brest, S. Greiner, B. Boskovic, M. Mernik, V. Zumer, Self adapting control parameters in differential evolution: A comparative study on numerical benchmark problems, IEEE Transactions on Evolutionary Computation, 10 (6), pp. 646–657, 2006.10.1109/TEVC.2006.872133Open DOISearch in Google Scholar

[18] A. Zamuda, J. Brest, Self-adaptive control parameters randomization frequency and propagations in differential evolution. Swarm and Evolutionary Computation, 25(1), pp. 72–99, 2015.10.1016/j.swevo.2015.10.007Search in Google Scholar

[19] A. K. Qin, V. L. Huang, P. N. Suganthan, Differential evolution algorithm with strategy adaptation for global numerical optimization, IEEE Transactions on Evolutionary Computation 13, pp. 398–417, 2009.10.1109/TEVC.2008.927706Open DOISearch in Google Scholar

[20] M. G. H. Omran, A. Salman, A. P. Engelbrecht, Self-adaptive differential evolution, in: Computational Intelligence and Security, PT 1, Proceedings Lecture Notes in Artificial Intelligence, pp. 192-199, 2005.10.1007/11596448_28Search in Google Scholar

[21] D. Zaharie, Control of population diversity and adaptation in differential evolution algorithms, in: Proceedings of the 9th International Conference on Soft Computing, Brno, pp. 41-46, 2003.Search in Google Scholar

[22] J. Tvrdik, Adaptation in differential evolution: a numerical comparison, Applied Soft Computing 9, pp. 1149–1155, 2009.10.1016/j.asoc.2009.02.010Open DOISearch in Google Scholar

[23] R. Mallipeddi, P. N. Suganthana, Q. K. Pan, M. F. Tasgetiren, Differential evolution algorithm with ensemble of parameters and mutation strategies, Applied Soft Computing, 11 (2), pp. 1679–1696, 2011.10.1016/j.asoc.2010.04.024Open DOISearch in Google Scholar

[24] R. Storn, K. Price, Differential evolution A simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, 11, pp. 341–359, 1997.10.1023/A:1008202821328Open DOISearch in Google Scholar

[25] J. Lampinen, I. Zelinka, On stagnation of the differential evolution algorithm, in: Proceedings of MENDEL 2000, 6th International Mendel Conference on Soft Computing, pp. 76–83, 2000.Search in Google Scholar

[26] J. Ronkkonen, S. Kukkonen, K. V. Price, Real parameter optimization with differential evolution, in Proceedings of IEEE Congress on Evolutionary Computation, 1, pp. 506–513, 2005.Search in Google Scholar

[27] J. Liu, J. Lampinen, A Fuzzy Adaptive Differential Evolution Algorithm, in: Soft Computing, A Fusion of Foundations, Methodologies and Applications, 9 (6), pp. 448–462, 2005.10.1007/s00500-004-0363-xOpen DOISearch in Google Scholar

[28] F. Neri, V. Tirronen, Recent advances in differential evolution: a survey and experimental analysis Artificial Intelligence Review, 33 (1-2), pp. 61–106, 2010.10.1007/s10462-009-9137-2Open DOISearch in Google Scholar

[29] J. Ronkkonen, J. Lampinen, On using normally distributed mutation step length for the differential evolution algorithm, in: Proceedings of the 9th Int. Conf. on Soft Computuing MENDEL, Brno, Czech Republic, pp. 11–18, 2003.Search in Google Scholar

[30] A. K. Qin, P. N. Suganthan, Self-adaptive Differential Evolution Algorithm for Numerical Optimization, in: Proceedings of the IEEE Congress on Evolutionary Computation, 2005.Search in Google Scholar

[31] M. M. Ali, A. Trn, Population set based global optimization algorithms: Some modifications and numerical studies, Journal of Computers and Operations Research, 31 (10), pp. 1703–1725, 2004.Search in Google Scholar

[32] U. K. Chakraborty, Advances in Differential Evolution, in: Differential Evolution Research-Trends and Open Questions, Springer, pp. 11–12, 2008.Search in Google Scholar

[33] D. Dawar, S. A. Ludwig, Differential evolution with dither and annealed scale factor, in: Proceedings of the IEEE Symposium Series on Computational Intelligence, Orlando, Florida, U.S.A., pp. 1–8, 2014.10.1109/SDE.2014.7031528Search in Google Scholar

[34] J. Teo, Exploring dynamic self-adaptive populations in differential evolution, Soft Computing - A Fusion of Foundations, Methodologies and Applications, 10 (8), pp. 673–686, 2006.10.1007/s00500-005-0537-1Search in Google Scholar

[35] J. Brest, M. S. Mauec, Population size reduction for the differential evolution algorithm, Applied Intelligence, 29 (3), pp. 228–247, 2008.10.1007/s10489-007-0091-xOpen DOISearch in Google Scholar

[36] J. Zhang, A. C. Sanderson, JADE: Adaptive differential evolution with optional external archive, IEEE Transaction on Evolutionary Computation, 13 (5), pp. 945-958, 2009.10.1109/TEVC.2009.2014613Open DOISearch in Google Scholar

[37] E. Mezura-Montes, J. Velazquez-Reyes, C. A. Coello Coello, A comparative study of differential evolution variants for global optimization, in GECCO, pp. 485–492, 2006.10.1145/1143997.1144086Search in Google Scholar

[38] F. Peng, K. Tang, G. Chen, X. Yao, Multi-start JADE with knowledge transfer for numerical optimization, in: Proceedings of the IEEE CEC, pp. 1889–1895, 2009.Search in Google Scholar

[39] Z. Yang, J. Zhang, K. Tang, X. Yao, A. C. Sanderson, An adaptive coevolutionary differential evolution algorithm for large-scale optimization, in: Proceedings of the IEEE CEC, pp. 102–109, 2009.10.1109/CEC.2009.4982936Search in Google Scholar

[40] W. Gong, Z. Cai, C. X. Ling, H. Li, Enhanced differential evolution with adaptive strategies for numerical optimization, IEEE Transactions on Systems, Man, and Cybernetics, PartB, 41 (2), pp. 397–413, 2011.10.1109/TSMCB.2010.205636720837448Search in Google Scholar

[41] J. Zhang, V. Avasarala, A. C. Sanderson, T. Mullen, Differential evolution for discrete optimization: An experimental study on combinatorial auction problems, in: Proceedings of the IEEE CEC, pp. 2794–2800, 2008.Search in Google Scholar

[42] J. Zhang, A. C. Sanderson, Self-adaptive multiobjective differential evolution with direction information provided by archived inferior solutions, in: Proceedings of the IEEE CEC, pp. 2801–2810, 2008.Search in Google Scholar

[43] R. Tanabe, A. Fukunaga, Success-History Based Parameter Adaptation for Differential Evolution, in: Proceedings of the IEEE CEC, pp. 71–78, 2013.10.1109/CEC.2013.6557555Search in Google Scholar

[44] R. Tanabe, A. Fukunaga, Evaluating the performance of SHADE on CEC 2013 benchmark problems, in: Proceedings of the IEEE CEC, pp. 1952–1959, 2013.Search in Google Scholar

[45] A. Auger, N. Hansen, A Restart CMA Evolution Strategy With Increasing Population Size, in: Proceedings of the IEEE CEC, pp. 1769–1776, 2005.Search in Google Scholar

[46] C. Garca-Martnez, M. Lozano, F. Herrera, D. Molina, A. M. Sanchez, Global and local real-coded genetic algorithms based on parent-centric crossover operators, European Journal of Operations Research, 185 (3), pp. 1088–1113, 2008.Search in Google Scholar

[47] M. A. M. de Oca, T. Stutzle, K. V. den Enden, M. Dorigo, Incremental Social Learning in Particle Swarms, IEEE Transactions on Systems, Man, and Cybernetics, PartB, 41 (2), pp. 368–384, 2011.10.1109/TSMCB.2010.205584820875976Open DOISearch in Google Scholar

[48] J. L. J. Laredo, C. Fernandes, J. J. M. Guervos, C. Gagne, Improving Genetic Algorithms Performance via Deterministic Population Shrinkage, in: Proceedings of the GECCO, pp. 819–826, 2009.10.1145/1569901.1570014Search in Google Scholar

[49] R. Tanabe, A. Fukunaga, Improving the Search Performance of SHADE Using Linear Population Size Reduction, in: Proceedings of the IEEE CEC, pp. 1658–1665, 2014.Search in Google Scholar

[50] J. Brest, A. Zamuda, B. Boskovic, M. S. Maucec, V. Zumer, Highdimensional real-parameter optimization using self-adaptive differential evolution algorithm with population size reduction, in: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2032–2039, 2008.Search in Google Scholar

[51] A. Zamuda, J. Brest. Population Reduction Differential Evolution with Multiple Mutation Strategies in Real World Industry Challenges. Artificial Intelligence and Soft Computing – ICAISC 2012, 7269, pp. 154–161, 2012.10.1007/978-3-642-29353-5_18Search in Google Scholar

[52] J. J. Liang, B.Y. Qu, P. N. Suganthan, A. G. Hernandez-Daz, Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang, 2013.Search in Google Scholar

[53] A. K. Qin, Xiaodong Li, Differential Evolution on the CEC-2013 Single-Objective Continuous Optimization Testbed, IEEE Congress on Evolutionary Computation, Cancun, Mexico, June 20-23, 2013.10.1109/CEC.2013.6557689Search in Google Scholar

[54] M. Friedman, The use of ranks to avoid the assumption of normality implicit in the analysis of variance, Journal of the American Statistical Association 32, pp. 674–701, 1937.10.1080/01621459.1937.10503522Search in Google Scholar

[55] D. J. Sheskin, Handbook of Parametric and Nonparametric Statistical Procedures, 4th ed., Chapman and Hall/CRC, 2006.Search in Google Scholar

[56] J. H. Zar, Biostatistical Analysis, Prentice Hall, 2009.Search in Google Scholar

[57] Y. Hochberg, A sharper Bonferroni procedure for multiple tests of significance, Biometrika, pp. 800–803, 1988.10.1093/biomet/75.4.800Search in Google Scholar

[58] J. Derrac, S. Garca, D. Molina, F. Herrera, A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm and Evolutionary Computation, vol 1, pp. 3–18, 2011.10.1016/j.swevo.2011.02.002Search in Google Scholar

[59] B. V. Babu, S. A. Munawar, Optimal design of shell-and-tube heat exchangers bu different strategies of Differential Evolution, Technical Report PILANI -333 031, Department of chemical engineering, BITS, Rajasthan, India, 2001.Search in Google Scholar

[60] J. Vesterstrom, R. A. Thomson, Comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems, in: Proceedings of the IEEE Congress on Evolutionary Computation, 1980–1987, 2004.Search in Google Scholar

[61] X. F. Xie, W. J. Zhang. SWAF: Swarm algorithm framework for numerical optimization, in: Proceedings of the Genetic Evolutionary Computation Conference, Part I, pp. 238–250, 2004.10.1007/978-3-540-24854-5_21Search in Google Scholar

[62] A. Zamuda, J. Brest, B. Bokovic, V. umer. Large scale global optimization using differential evolution with self-adaptation and cooperative coevolution, in: Proceedings of the 2008 IEEE World Congress on Computational Intelligence, pp. 3719–3726, 2008.Search in Google Scholar

[63] Z. Yang, K. Tang, X. Yao. Self-adaptive differential evolution with neighborhood search. In Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1110–1116, 2008.Search in Google Scholar

[64] A. Iorio, X. Li, Solving rotated multi-objective optimization problems using differential evolution, in: Australian Conference on Artificial Intelligence, Cairns, Australia, pp. 861–872, 2004.10.1007/978-3-540-30549-1_74Search in Google Scholar

[65] S. Das, A. Abraham, U.K. Chakraborthy, Differential evolution using a neighborhood-based mutation operator, IEEE Transactions on Evolutionary Computation 13, pp. 526–553, 2009.10.1109/TEVC.2008.2009457Open DOISearch in Google Scholar

[66] D. H. Wolpert, W. G. 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

eISSN:
2083-2567
Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Computer Sciences, Databases and Data Mining, Artificial Intelligence