1- Chen Y, Li L, Xiao J, Yang Y, Liang J, Li T. Particle swarm optimizer with crossover operation. Journal of Engineering Applications of Artificial Intelligence. 2018;70:159-169. [
Link] [
DOI:10.1016/j.engappai.2018.01.009]
Storn R, Price K. Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization. 1997;11(4):341-359. [
Link] [
DOI:10.1023/A:1008202821328]
Shi H, Liu S, Wu H, Li R, Liu S, Kwok N, et al. Oscillatory Particle Swarm Optimizer. Applied Soft Computing. 2018;73:316-327. [
Link] [
DOI:10.1016/j.asoc.2018.08.037]
Ratnaweera A, Halgamuge SK, Watson HC. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Transactions on Evolutionary Computation. 2004;8(3):240-255. [
Link] [
DOI:10.1109/TEVC.2004.826071]
Nesamalar J, Venkatesh P, Raja S. Managing multi-line power congestion by using Hybrid Nelder–Mead–Fuzzy Adaptive Particle Swarm Optimization (HNM-FAPSO). Applied Soft Computing. 2016;43:222-234. [
Link] [
DOI:10.1016/j.asoc.2016.02.013]
Wang H, Sun H, Li C, Rahnamayan S, Pan J. Diversity enhanced particle swarm optimization with neighborhood search. Information Sciences. 2013;223:119-135. [
Link] [
DOI:10.1016/j.ins.2012.10.012]
Luo W, Sun J, Bu C, Liang H. Species-based particle swarm optimizer enhanced by memory for dynamic optimization. Applied Soft Computing. 2016;47:130-140. [
Link] [
DOI:10.1016/j.asoc.2016.05.032]
Wang L, Yang B, Orchard J. Particle swarm optimization using dynamic tournament topology. Applied Soft Computing. 2016;48:584-596. [
Link] [
DOI:10.1016/j.asoc.2016.07.041]
Li NJ, Wang WJ, James Hsu CC, Chang W, Chou HG, Chang JW. Enhanced particle swarm optimizer incorporating a weighted particle. Neurocomputing. 2014;124:218-227. [
Link] [
DOI:10.1016/j.neucom.2013.07.005]
Yi W, Zhou Y, Gao L, Li X, Mou J. An improved adaptive differential evolution algorithm for continuous optimization. Expert Systems with Applications. 2016;44:1-12. [
Link] [
DOI:10.1016/j.eswa.2015.09.031]
Salehpour M, Jamali A, Bagheri A, Nariman-zadeh N. A new adaptive differential evolution optimization algorithm based on fuzzy inference system. Engineering Science and Technology, an International Journal, 2017;20,(2):587-597. [
Link]
Brest J, Greiner S, Boskovic B, Mernik M, Zumer V. Self-Adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation. 2006;10(6):646-657. [
Link] [
DOI:10.1109/TEVC.2006.872133]
Zou D., Li S., Wang G.-G., Li Z., Ouyang H. An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects. Applied Energy. 2016;181:375-390. [
Link] [
DOI:10.1016/j.apenergy.2016.08.067]
Thangaraj R, Pant M, Abraham A, Bouvry p. Particle swarm optimization: Hybridization perspectives and experimental illustrations. Applied Mathematics and Computation. 2011;217:5208-5226. [
Link] [
DOI:10.1016/j.amc.2010.12.053]
Jang JSR. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics. 1993;23(3):665-685. [
Link] [
DOI:10.1109/21.256541]
Wang WC, Chau K.W, Cheng CT, Qiu L. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology. 2009;374(3):294-306. [
Link] [
DOI:10.1016/j.jhydrol.2009.06.019]
Babuška R, Verbruggen H. Neuro-fuzzy methods for nonlinear system identification. Annual Reviews in Control. 2003;27(1):73-85. [
Link] [
DOI:10.1016/S1367-5788(03)00009-9]
