References
- S. He, Q. H. Wu, and J. Saunders, “Group search optimizer: an optimization algorithm inspired by animal searching behavior,” IEEE Trans. Evolution. Comput., Vol. 13, pp. 973–990, 2009. doi: 10.1109/TEVC.2009.2011992
- P. Civicioglu, “Backtracking search optimization algorithm for numerical optimization problems,” Appl. Math. Comput., Vol. 219, pp. 8121–8144, 2013.
- T.-Y. Chen and T.-M. Chi, “On the improvements of the particle swarm optimization algorithm,” Advanc. Eng. Soft., Vol. 41, pp. 229–239, 2010. doi: 10.1016/j.advengsoft.2009.08.003
- M. Guerrero, F. G. Montoya, R. Baños, A. Alcayde, and C. Gil, “Adaptive community detection in complex networks using genetic algorithms,” Neurocomputing, Vol. 266, pp. 101–113, 2017. doi: 10.1016/j.neucom.2017.05.029
- K. Zare, M. T. Haque, and E. Davoodi, “Solving non-convex economic dispatch problem with valve point effects using modified group search optimizer method,” Elect. Power Syst. Res., Vol. 84, pp. 83–89, 2012. doi: 10.1016/j.epsr.2011.10.004
- F. Erchiqui, “Application of genetic and simulated annealing algorithms for optimization of infrared heating stage in thermoforming process,” Appl. Therm. Eng., Vol. 128, pp. 1263–1272, 2018. doi: 10.1016/j.applthermaleng.2017.09.102
- P. B. Miranda and R. B. Prudêncio, “Generation of particle swarm optimization algorithms: an experimental study using grammar-guided genetic programming,” Appl. Soft Comput., Vol. 60, pp. 281–296, 2017. doi: 10.1016/j.asoc.2017.06.040
- W. Ye, W. Feng, and S. Fan, “A novel multi-swarm particle swarm optimization with dynamic learning strategy,” Appl. Soft Comput., Vol. 61, pp. 832–843, 2017. doi: 10.1016/j.asoc.2017.08.051
- E. Atashpaz-Gargari and C. Lucas, “Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition,” in Evolutionary computation, 2007. CEC 2007. IEEE Congress on, 2007, pp. 4661– 4667.
- M. Mehdinejad, B. Mohammadi-Ivatloo, R. Dadashzadeh-Bonab, and K. Zare, “Solution of optimal reactive power dispatch of power systems using hybrid particle swarm optimization and imperialist competitive algorithms,” Int. J. Electr. Power Energ. Syst., Vol. 83, pp. 104–116, 2016. doi: 10.1016/j.ijepes.2016.03.039
- J. Sadeghi, S. M. Mousavi, and S. T. A. Niaki, “Optimizing an inventory model with fuzzy demand, backordering, and discount using a hybrid imperialist competitive algorithm,” Appl. Math. Model., Vol. 40, pp. 7318–7335, 2016. doi: 10.1016/j.apm.2016.03.013
- M. Etesami, N. Farokhnia, and S. H. Fathi, “Colonial competitive algorithm development toward harmonic minimization in multilevel inverters,” IEEE Trans. Indust. Informat., Vol. 11, pp. 459–466, 2015.
- M. Clerc and J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space,” IEEE Trans. Evol. Comput., Vol. 6, pp. 58–73, 2002. doi: 10.1109/4235.985692
- Y. Ma, C. Jiang, Z. Hou, and C. Wang, “The formulation of the optimal strategies for the electricity producers based on the particle swarm optimization algorithm,” IEEE Trans. Power Syst., Vol. 21, pp. 1663–1671, 2006. doi: 10.1109/TPWRS.2006.883676
- K. T. Chaturvedi, M. Pandit, and L. Srivastava, “Particle swarm optimization with time varying acceleration coefficients for non-convex economic power dispatch,” Int. J. Electr. Power Energ. Syst., Vol. 31, pp. 249–257, 2009. doi: 10.1016/j.ijepes.2009.01.010
- X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Trans. Evol.Comput., Vol. 3, pp. 82–102, 1999. doi: 10.1109/4235.771163
- M. Jamil and X.-S. Yang, “A literature survey of benchmark functions for global optimisation problems,” Int. J. Math Model Numer. Optimis., Vol. 4, pp. 150–194, 2013.
- J. Brest, S. Greiner, B. Boskovic, M. Mernik, and V. Zumer, “Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems,” IEEE Trans. Evol. Comput., Vol. 10, pp. 646–657, 2006. doi: 10.1109/TEVC.2006.872133
- W. Gong, Z. Cai, and L. Jiang, “Enhancing the performance of differential evolution using orthogonal design method,” Appl. Math. Comput., Vol. 206, pp. 56–69, 2008.
- G. Li, P. Niu, and X. Xiao, “Development and investigation of efficient artificial bee colony algorithm for numerical function optimization,” Appl. Soft Comput., Vol. 12, pp. 320–332, 2012. doi: 10.1016/j.asoc.2011.08.040
- E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi, “GSA: a gravitational search algorithm,” Inform. Sci., Vol. 179, pp. 2232–2248, 2009. doi: 10.1016/j.ins.2009.03.004
- M. Xi, J. Sun, and W. Xu, “An improved quantum-behaved particle swarm optimization algorithm with weighted mean best position,” Appl. Math. Comput., Vol. 205, pp. 751–759, 2008.
- D. Whitley, S. Rana, J. Dzubera, and K. E. Mathias, “Evaluating evolutionary algorithms,” Arti. Intell., Vol. 85, pp. 245–276, 1996. doi: 10.1016/0004-3702(95)00124-7
- J. J. Liang, A. K. Qin, P. N. Suganthan, and S. Baskar, “Comprehensive learning particle swarm optimizer for global optimization of multimodal functions,” IEEE Trans. Evolut. Comput., Vol. 10, pp. 281–295, 2006. doi: 10.1109/TEVC.2005.857610
- X. Yao and Y. Liu, “Scaling up evolutionary programming algorithms,” in International Conference on Evolutionary Programming, 1998, pp. 103– 112.
- J. Derrac, S. García, D. Molina, and F. Herrera, “A Practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms,” Swarm Evolut.Comput., Vol. 1, pp. 3–18, 2011. doi: 10.1016/j.swevo.2011.02.002
- L. Luo, X. Hou, J. Zhong, W. Cai, and J. Ma, “Sampling-based adaptive bounding evolutionary algorithm for continuous optimization problems,” Inform. Sci., Vol. 382, pp. 216–233, 2017. doi: 10.1016/j.ins.2016.12.023
- S. Xu, Y. Wang, and P. Lu, “Improved imperialist competitive algorithm with mutation operator for continuous optimization problems,” Neur. Comput. Appl., Vol. 28, pp. 1667–1682, 2017. doi: 10.1007/s00521-015-2138-y
- H.-B. Ouyang, L.-Q. Gao, X.-Y. Kong, D.-X. Zou, and S. Li, “Teaching-learning ,” Appl. Math. Comput., Vol. 265, pp. 533–556, 2015. based optimization with global crossover for global optimization problems
- X. Liu and M. Fu, “Cuckoo search algorithm based on frog leaping local search and chaos theory,” Appl. Math. Comput., Vol. 2, no. 66, pp. 1083–1092, 2015.
- W.-F. Gao, L.-L. Huang, S.-Y. Liu, F. T. Chan, C. Dai, and X. Shan, “Artificial bee colony algorithm with multiple search strategies,” Appl. Math. Comput., Vol. 271, pp. 269–287, 2015.
- W.-L. Xiang and M.-Q. An, “An efficient and robust artificial bee colony algorithm for numerical optimization,” Comput. Oper. Res., Vol. 40, pp. 1256–1265, 2013. doi: 10.1016/j.cor.2012.12.006
- W. Gong, Z. Cai, C. X. Ling, and H. Li, “A real-coded biogeography-based optimization with mutation,” Appl. Math. Comput., Vol. 216, pp. 2749–2758, 2010.