An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization

[1] Al-Dabbagh R. D., Neri F., Idris N., Baba M. S., Algorithmic design issues in adaptive differential evolution schemes: Review and taxonomy, Swarm and Evolutionary Computation, 43, 2018, 284-311.10.1016/j.swevo.2018.03.008Search in Google Scholar

[2] Boussa¨ıd I., Lepagnot J., Siarry P., A survey on optimization metaheuristics, Information sciences, 237, 2013, 82–117.10.1016/j.ins.2013.02.041Search in Google Scholar

[3] Brest J., Greiner S., Bokovi B., Mernik M.,Žumer V., Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems, IEEE Trans Evol Comput, 10, 6, 2006, 646-657.10.1109/TEVC.2006.872133Search in Google Scholar

[4] Das S., Konar A., Chakraborty U. K., Two improved differential evolution schemes for faster global search, in : Proceedings of the 2005 conference on genetic and evolutionary computation, 2005, 991-998.10.1145/1068009.1068177Search in Google Scholar

[5] Das S., Mullick S.S., Suganthan P., Recent advances in differential evolution-An updated survey, Swarm and Evolutionary Computation, 27, 2016, 1-30.10.1016/j.swevo.2016.01.004Search in Google Scholar

[6] Dennis J. E., Schnabel R. B., Numerical methods for unconstrained optimization and nonlinear equations, SIAM,Philadelphia, 1996.10.1137/1.9781611971200Search in Google Scholar

[7] Gamperle R., Muller S. D., Koumoutsakos P., A parameter study for differential evolution, in: A. Gremla, N. E. Mastorakis (eds.), Advances in intelligent systems, fuzzy systems, evolutionary computation, WSEAS Press, Interlaken, 2002, 293-298.Search in Google Scholar

[8] Hamm L., Brorsen B. W., Hagan M. T., Comparison of stochastic global optimization methods to estimate neural network weights, Neural Process Lett, 26, 2007, 145-158.10.1007/s11063-007-9048-7Search in Google Scholar

[9] Hirsch M. J., Pardalos P. M., Resende M. G. C., Solving systems of nonlinear equations with continuous GRASP, Nonlinear Analysis: Real World Applications, 10, 2009, 2000–2006.10.1016/j.nonrwa.2008.03.006Search in Google Scholar

[10] Lampinen J., Zelinka I., On stagnation of the differential evolution algorithm, in: R. Matouek, P. Omera (eds.), Proceedings of Mendel 2000, 6th international conference on soft computing, 2000, 76-83.Search in Google Scholar

[11] Leon M., Xiong N., Adapting differential evolution algorithms for continuous optimization via greedy adjustment of control parameters, Journal of artificial intelligence and soft computing research, 6, 2, 2016, 103-118.10.1515/jaiscr-2016-0009Search in Google Scholar

[12] Li W., Li S., Chen Z., Zhong L., Ouyang C., Self-feedback differential evolution adapting to fitness landscape characteristics, Soft Comput, 23, 2019, 1151–1163.10.1007/s00500-017-2833-ySearch in Google Scholar

[13] Mallipeddi R., Suganthan P. N., Empirical study on the e ect of population size on differential evolution algorithm, in: Proceeding of the IEEE congress on evolutionary computation (CEC-2008), 2008, 3663–3670.10.1109/CEC.2008.4631294Search in Google Scholar

[14] Meintjes K., Morgan A., Chemical equilibrium systems as numerical test problems, ACM Transaction on Mathematical Software, 16, 1990, 143–151.10.1145/78928.78930Search in Google Scholar

[15] Mezura-Montes E., Velzquez-Reyes J., Coello Coello C. A., A comparative study of differential evolution variants for global optimization, in: Proceedings of the 8th annual genetic and evolutionary computation conference (GECCO-2006), 2006, 485-492.10.1145/1143997.1144086Search in Google Scholar

[16] Morgan A., Shapiro V., Box-bisection for solving second-degree systems and the problem of clustering, ACM Transaction on Mathematical Software, 13, 1987, 152–167.10.1145/328512.328521Search in Google Scholar

[17] Nanda S. J., Panda G., A survey on nature inspired metaheuristic algorithms for partitional clustering, Swarm and Evolutionary Computation, 16, 2014, 1-18.10.1016/j.swevo.2013.11.003Search in Google Scholar

[18] Neri F., Tirronen V., Recent advances in differential evolution: A survey and experimental analysis, Artif Intell Rev, 33, 2010, 61-106.10.1007/s10462-009-9137-2Search in Google Scholar

[19] Oliveira H. A., Petraglia A., Solving nonlinear systems of functional equations with fuzzy adaptive simulated annealing, Applied Soft Computing, 13, 2013, 4349–4357.10.1016/j.asoc.2013.06.018Search in Google Scholar

[20] Ortega J. M., Rheinboldt W. C., Iterative solution of nonlinear equations inseveral variables, SIAM, Philadelphia, 2000.10.1137/1.9780898719468Search in Google Scholar

[21] Pramanik S., Kinematic synthesis of a six-member mechanism for automotive steering, ASME Journal of Mechanical Design, 124, 2002, 642–645.10.1115/1.1503372Search in Google Scholar

[22] Qin A.K., Huang V. L., Suganthan P. N., Differential evolution algorithm with strategy adaptation for global numerical optimization, IEEE Trans Evol Comput, 13, 2, 2009, 398-417.10.1109/TEVC.2008.927706Search in Google Scholar

[23] Ronkkonen J., Kukkonen S., Price K. V., Real parameter optimization with differential evolution, in : Proceedings of the IEEE congress evolutionary computation (CEC-2005) vol 1, IEEE Press, 2005, 506-513.Search in Google Scholar

[24] Storn R., Differential evolution research-trends and open questions, in: U. K. Chakraborty (ed.), Advances in Differential Evolution, Springer, Berlin, 2008, 1-31.10.1007/978-3-540-68830-3_1Search in Google Scholar

[25] Storn R., Price K., Differential evolution – A simple and e cient adaptive scheme for global optimization over continuous spaces, Technical Report TR-95-012, ICSI, 1995.Search in Google Scholar

[26] Storn R., Price K., Differential evolution: A simple and e cient heuristic for global optimization over continuous spaces, J Glob Optim, 11, 4, 1997, 341–359.10.1023/A:1008202821328Search in Google Scholar

[27] Venter G., Review of optimization techniques, in: R. Blockley, S. Wei (eds.), Encyclopedia of aerospace engineering, Wiley and Sons, 2010, doi: 10.1002/9780470686652.eae495.10.1002/9780470686652.eae495Search in Google Scholar

[28] Verschelde J., Verlinden P., Cools R., Homotopies exploiting newton polytopes for solving sparse polynomial systems, SIAM J. Numer. Anal, 31, 1994, 915–930.10.1137/0731049Search in Google Scholar

[29] Wongpen J., Wetweerapong J., Puphasuk P., Finding a maximum clique in social networks using a modified differential evolution algorithm, WSEAS Transactions on Systems and Control, 14, 2019, 333–342.Search in Google Scholar

[30] Zhang J., Sanderson A. C., JADE: adaptive differential evolution with optional external archive, IEEE Trans Evol Comput, 13, 5, 2009, 945-958.10.1109/TEVC.2009.2014613Search in Google Scholar

[31] Zielinski K., Weitkemper P., Laur R., Kammeyer K., Parameter study for differential evolution using a power allocation problem including interference cancellation, in: IEEE congress on evolutionary computation (CEC-2006), 2006, 1857-1864.Search in Google Scholar