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Storn and K. Price, \u201cDifferential evolution: A simple and efficient adaptive scheme for global optimization over continuous spaces,\u201d Tech. Rep., TR-95-012, University of California, Berkeley, California, 1995."},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] F. Arce, E. Zamora, H. Sossa, and R. Barr\u00f3n, \u201cDifferential evolution training algorithm for dendrite morphological neural networks,\u201d Applied Soft Computing, vol.68, pp.303-313, 2018. 10.1016\/j.asoc.2018.03.033","DOI":"10.1016\/j.asoc.2018.03.033"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] J. Yi, D. Jian, and S. Zhenhong, \u201cPattern synthesis of MIMO radar based on chaotic differential evolution algorithm,\u201d Optik, vol.140, pp.794-801, 2017. 10.1016\/j.ijleo.2017.02.088","DOI":"10.1016\/j.ijleo.2017.02.088"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] K.Y. Kok and P. Rajendran, \u201cDifferential-evolution control parameter optimization for unmanned aerial vehicle path planning,\u201d PLOS ONE, pp.1-12, 2016.","DOI":"10.1371\/journal.pone.0150558"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] S. Wang, Y. Li, and H. Yang, \u201cSelf-adaptive differential evolution algorithm with improved mutation mode,\u201d Applied Intelligence, vol.47, no.3, pp.644-658, 2017. 10.1007\/s10489-017-0914-3","DOI":"10.1007\/s10489-017-0914-3"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] R. Tang, \u201cDecentralizing and coevolving differential evolution for large-scale global optimization problems,\u201d Appl Intell, vol.47, no.4, pp.1208-1223, 2017. 10.1007\/s10489-017-0953-9","DOI":"10.1007\/s10489-017-0953-9"},{"key":"7","unstructured":"[7] A.W. Mohamed and A.K. Mohamed, \u201cAdaptive guided differential evolution algorithm with novel mutation for numerical optimization,\u201d International Journal of Machine Learning and Cybernetics, pp.1-23, 2017."},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] X. He and Y. Zhou, \u201cEnhancing the performance of differential evolution with covariance matrix self-adaptation,\u201d Applied Soft Computing, vol.64, pp.227-243, 2018. 10.1016\/j.asoc.2017.11.050","DOI":"10.1016\/j.asoc.2017.11.050"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] A.W. Mohamed and P.N. Suganthan, \u201cReal-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation,\u201d Soft Comput, vol.22, no.10, pp.3215-3235, 2018. 10.1007\/s00500-017-2777-2","DOI":"10.1007\/s00500-017-2777-2"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] S.M. Elsayed, R.A. Sarker, and D.L. Essam, \u201cA self-adaptive combined strategies algorithm for constrained optimization using differential evolution,\u201d Applied Mathematics and Computation, vol.241, pp.267-282, 2014. 10.1016\/j.amc.2014.05.018","DOI":"10.1016\/j.amc.2014.05.018"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] G. Wu, R. Mallipeddi, P.N. Suganthan, R. Wang, and H. Chen, \u201cDifferential evolution with multi-population based ensemble of mutation strategies,\u201d Information Sciences, vol.329, pp.329-345, 2016. 10.1016\/j.ins.2015.09.009","DOI":"10.1016\/j.ins.2015.09.009"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] M.-F. Yeh, H.-C. Lu, T.-H. Chen, and M.-S. Leu, \u201cModified Gaussian barebones differential evolution with hybrid crossover strategy,\u201d Proc. 2016 International Conference on Machine Learning and Cybernetics, pp.7-12, 2017. 10.1109\/icmlc.2016.7860869","DOI":"10.1109\/ICMLC.2016.7860869"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] L. Cui, G. Li, Z. Zhu, Q. Lin, K.-C. Wong, J. Chen, N. Lu, and J. Lu, \u201cAdaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism,\u201d Information Sciences, vol.422, pp.122-142, 2018. 10.1016\/j.ins.2017.09.002","DOI":"10.1016\/j.ins.2017.09.002"},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] G. Wu, X. Shen, H. Li, H. Chen, A. Lin, and P.N. Suganthan, \u201cEnsemble of differential evolution variants,\u201d Information Sciences, vol.423, pp.172-186, 2018. 10.1016\/j.ins.2017.09.053","DOI":"10.1016\/j.ins.2017.09.053"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] W. Gong and Z. Cai, \u201cDifferential Evolution With Ranking-Based Mutation Operators,\u201d IEEE Trans. Cybern., vol.43, no.6, pp.2066-2081, 2013. 10.1109\/tcyb.2013.2239988","DOI":"10.1109\/TCYB.2013.2239988"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] R. Tanabe and A. Fukunaga, \u201cSuccess-History Based Parameter Adaptation for Differential Evolution,\u201d IEEE Congress on Evolutionary Computation, (CEC), pp.71-78, 2013. 10.1109\/cec.2013.6557555","DOI":"10.1109\/CEC.2013.6557555"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] W. Gong, Z. Cai, and Y. Wang, \u201cRepairing the crossover rate in adaptive differential evolution,\u201d Applied Soft Computing, vol.15, pp.149-168, 2014. 10.1016\/j.asoc.2013.11.005","DOI":"10.1016\/j.asoc.2013.11.005"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] N.H. Awad, M.Z. Ali, P.N. Suganthan, and R.G. Reynolds, \u201cAn ensemble sinusoidal parameter adaptation incorporated with L-SHADE for solving CEC2014 benchmark problems,\u201d Proc. IEEE Congress on Evolutionary Computation, pp.2958-2965, 2016. 10.1109\/cec.2016.7744163","DOI":"10.1109\/CEC.2016.7744163"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] L. Chen, S. Zhao, W. Zhu, Y. Liu, and W. Zhang, \u201cA self-Adaptive differential evolution algorithm for parameters identification of stochastic genetic regulatory networks with random delays,\u201d Arabian Journal for Science and Engineering, vol.39, no.2, pp.821-835, 2014. 10.1007\/s13369-013-0803-y","DOI":"10.1007\/s13369-013-0803-y"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] X. Wang and S. Zhao, \u201cDifferential evolution algorithm with self-adaptive population resizing mechanism,\u201d Mathematical Problems in Engineering, 419372, 2013.","DOI":"10.1155\/2013\/419372"},{"key":"21","doi-asserted-by":"publisher","unstructured":"[21] N.H. Awad, M.Z. Ali, and P.N. Suganthan, \u201cEnsemble of parameters in a sinusoidal differential evolution with niching-based population reduction,\u201d Swarm and Evolutionary Computation, vol.39, pp.141-156, 2018. 10.1016\/j.swevo.2017.09.009","DOI":"10.1016\/j.swevo.2017.09.009"},{"key":"22","doi-asserted-by":"publisher","unstructured":"[22] Y. Wang, H.-X. Li, T. Huang, and L. Li, \u201cDifferential evolution based on covariance matrix learning and bimodal distribution parameter setting,\u201d Appl. Soft Comput., vol.18, pp.232-247, 2014. 10.1016\/j.asoc.2014.01.038","DOI":"10.1016\/j.asoc.2014.01.038"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] Y.Q. Cai and J.H. Wang, \u201cDifferential evolution with hybrid linkage crossover,\u201d Inf. Sci, vol.320, pp.244-287, 2015. 10.1016\/j.ins.2015.05.026","DOI":"10.1016\/j.ins.2015.05.026"},{"key":"24","doi-asserted-by":"publisher","unstructured":"[24] S.-M. Guo and C.-C. Yang, \u201cEnhancing Differential Evolution Utilizing Eigenvector-Based Crossover Operator,\u201d IEEE Trans. Evol. Comput., vol.19, no.1, pp.31-49, 2015. 10.1109\/tevc.2013.2297160","DOI":"10.1109\/TEVC.2013.2297160"},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] Y. Xu, J.-A. Fang, W. Zhu, X. Wang, and L. Zhao, \u201cDifferential evolution using a superior-inferior crossover scheme,\u201d Comput Optim Appl., vol.61, no.1, pp.243-274, 2015. 10.1007\/s10589-014-9701-9","DOI":"10.1007\/s10589-014-9701-9"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] Q. Zhu, Q. Lin, Z. Du, Z. Liang, W. Wang, Z. Zhu, J. Chen, P. Huang, and Z. Ming, \u201cA novel adaptive hybrid crossover operator for multiobjective evolutionary algorithm,\u201d Information Sciences, vol.345, pp.177-198, 2016. 10.1016\/j.ins.2016.01.046","DOI":"10.1016\/j.ins.2016.01.046"},{"key":"27","doi-asserted-by":"publisher","unstructured":"[27] A. Ghosh, S. Das, S.S. Mullick, R. Mallipeddi, and A.K. Das, \u201cA switched parameter differential evolution with optional blending crossover for scalable numerical optimization,\u201d Applied Soft Computing, vol.57, pp.329-352, 2017. 10.1016\/j.asoc.2017.03.003","DOI":"10.1016\/j.asoc.2017.03.003"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] X. Qiu, K.C. Tan, and J.-X. Xu, \u201cMultiple Exponential Recombination for Differential Evolution,\u201d IEEE Trans. Cybern., vol.47, no.4, pp.995-1005, 2017. 10.1109\/tcyb.2016.2536167","DOI":"10.1109\/TCYB.2016.2536167"},{"key":"29","doi-asserted-by":"publisher","unstructured":"[29] X. Li and M. Yin, \u201cHybrid differential evolution with artificial bee colony and its application for design of a reconfigurable antenna array with discrete phase shifters,\u201d Iet Microwaves Antennas &amp; Propagation, vol.6, no.6, pp.1573-1582, 2012. 10.1049\/iet-map.2011.0611","DOI":"10.1049\/iet-map.2011.0611"},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] K. Vaisakh, P. Praveena, and K.N. Sujatah, \u201cDifferential evolution and bacterial foraging optimization based dynamic economic dispatch with non-smooth fuel cost functions,\u201d Swarm, Evolutionary, and Memetic Computing, pp.583-594, 2013. 10.1007\/978-3-319-03756-1_52","DOI":"10.1007\/978-3-319-03756-1_52"},{"key":"31","doi-asserted-by":"publisher","unstructured":"[31] A. Ponsich and C.A.C. Coello, \u201cA hybrid differential evolution-Tabu search algorithm for the solution of job-shop scheduling problems,\u201d Applied Soft Computing, vol.13, no.1, pp.462-474, 2013. 10.1016\/j.asoc.2012.07.034","DOI":"10.1016\/j.asoc.2012.07.034"},{"key":"32","unstructured":"[32] D.L. Le, N.D. Vo, T.H. Nguyen, and A.D. Le, \u201cA hybrid differential evolution and harmony search for non-convex economic dispatch problems,\u201d IEEE Conference on Power Engineering and Optimization, pp.238-243, 2013. 10.1109\/peoco.2013.6564550"},{"key":"33","doi-asserted-by":"publisher","unstructured":"[33] H. Nenavath and R.K. Jatoth, \u201cHybridizing sine cosine algorithm with differential evolution for global optimization and object tracking,\u201d Applied Soft Computing, vol.62, pp.1049-1043, 2018. 10.1016\/j.asoc.2017.09.039","DOI":"10.1016\/j.asoc.2017.09.039"},{"key":"34","unstructured":"[34] S. Mirjalili, \u201cDragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems,\u201d Neural Comput. Applic., vol.27, no.2, pp.1053-1073, 2016."},{"key":"35","unstructured":"[35] K. Price, R. Storn, and J. Lampinen, Differential Evolution: A Practical Approach to Global Optimization, Springer-VerlagR, Berlin, Germany, 2005."},{"key":"36","unstructured":"[36] R. Storn and K. Price, \u201cDifferential evolution,\u201d Int. Comput., Sci. Inst., Berkeley, CA, USA, 2010."},{"key":"37","doi-asserted-by":"publisher","unstructured":"[37] S. Das, A. Abraham, U.K. Chakraborty, and A. Konar, \u201cDifferential evolution using a neighborhood-based mutation operator,\u201d IEEE Trans. Evol. Comput., vol.13, no.3, pp.526-553, 2009. 10.1109\/tevc.2008.2009457","DOI":"10.1109\/TEVC.2008.2009457"},{"key":"38","unstructured":"[38] P.N. Suganthan, N. Hansen, J.J. Liang, K. Deb, Y.-P. Chen, A. Auger, and S. Tiwari, \u201cProblem definitions and evaluation criteria for the CEC 2005 Special Session on Real-Parameter Optimization,\u201d report no.2005005, 2005."},{"key":"39","unstructured":"[39] J.J. Liang, B.Y. Qu, P.N. Suganthan, and Q. Chen, \u201cProblem definition and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization,\u201d report no.201411B, 2015."},{"key":"40","unstructured":"[40] N.H. Awad, M.Z. Ali, P.N. Suganthan, J.J. Liang, and B.Y. Qu, \u201cProblem definitions and evaluation criteria for the CEC 2017 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization,\u201d 2016."},{"key":"41","doi-asserted-by":"publisher","unstructured":"[41] J. Derrac, S. Garc\u00eda, D. Molina, and F. Herrera, \u201cA practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms,\u201d Swarm Evolutionary Computation, vol.1, no.1, pp.3-18, 2011. 10.1016\/j.swevo.2011.02.002","DOI":"10.1016\/j.swevo.2011.02.002"},{"key":"42","doi-asserted-by":"publisher","unstructured":"[42] S. Mirjalili, \u201cMoth-flame optimization algorithm: A novel nature-inspired heuristic paradigm,\u201d Knowledge-Based Systems, vol.89, pp.228-249, 2015. 10.1016\/j.knosys.2015.07.006","DOI":"10.1016\/j.knosys.2015.07.006"},{"key":"43","doi-asserted-by":"crossref","unstructured":"[43] S. Mirjalili, S.M. Mirjalili, and A. Hatamlou, \u201cMulti-verse optimizer: A nature-inspired algorithm for global optimization,\u201d Neural Comput. Appli., vol.27, no.2, pp.495-513, 2016.","DOI":"10.1007\/s00521-015-1870-7"},{"key":"44","doi-asserted-by":"publisher","unstructured":"[44] S. Mirjalili, \u201cThe Ant Lion Optimizer,\u201d Advances in Engineering Software, vol.83, pp.80-98, 2015. 10.1016\/j.advengsoft.2015.01.010","DOI":"10.1016\/j.advengsoft.2015.01.010"},{"key":"45","doi-asserted-by":"publisher","unstructured":"[45] S. Mirjalili and A. Lewis, \u201cThe Whale Optimization Algorithm,\u201d Advances in Engineering Software, vol.95, pp.51-67, 2016. 10.1016\/j.advengsoft.2016.01.008","DOI":"10.1016\/j.advengsoft.2016.01.008"},{"key":"46","doi-asserted-by":"crossref","unstructured":"[46] S. 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