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JGO (Journal of Global Optimization)

  • Touré, C., Auger, A. and Hansen, N., 2022. Global linear convergence of evolution strategies with recombination on scaling-invariant functions. Journal of Global Optimization, pp.1-41. [ www ] ( ES | Continuous Optimization )
  • La Cruz, W., 2022. A genetic algorithm with a self-reproduction operator to solve systems of nonlinear equations. Journal of Global Optimization, pp.1-28. [ www ] ( GA )
  • Ramanauskas, M., Šešok, D., Žilinskas, J., Starikovičius, V., Kačeniauskas, A. and Belevičius, R., 2020. Global optimization of grillage-type foundations using a distributed genetic algorithm. Journal of Global Optimization, 77, pp.157-173. [ www ]
  • Ruiz, A.B., Saborido, R., Bermúdez, J.D., Luque, M. and Vercher, E., 2020. Preference-based evolutionary multi-objective optimization for portfolio selection: A new credibilistic model under investor preferences. Journal of Global Optimization, 76(2), pp.295-315. [ www ]
  • Yang, Z., Qiu, H., Gao, L., Jiang, C. and Zhang, J., 2019. Two-layer adaptive surrogate-assisted evolutionary algorithm for high-dimensional computationally expensive problems. Journal of Global Optimization, 74(2), pp.327-359. [ www ]
  • Bradford, E., Schweidtmann, A.M. and Lapkin, A., 2018. Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm. Journal of Global Optimization, 71(2), pp.407-438. [ www ]
  • Moreno, J.J., Ortega, G., Filatovas, E., Martínez, J.A. and Garzón, E.M., 2018. Improving the performance and energy of non-dominated sorting for evolutionary multiobjective optimization on GPU/CPU platforms. Journal of Global Optimization, 71(3), pp.631-649. [ www ]
  • Gerber, M. and Bornn, L., 2017. Improving simulated annealing through derandomization. Journal of Global Optimization, 68(1), pp.189-217. [ www ] ( SA )
  • Jie, H., Wu, Y., Zhao, J. and Ding, J., 2017. An efficient multi-objective PSO algorithm assisted by Kriging metamodel for expensive black-box problems. Journal of Global Optimization, 67(1), pp.399-423. [ www ] ( PSO )
  • Vandaele, A., Gillis, N., Glineur, F. and Tuyttens, D., 2016. Heuristics for exact nonnegative matrix factorization. Journal of Global Optimization, 65(2), pp.369-400. [ www ] ( SA )
  • Weise, T., Wu, Y., Chiong, R., Tang, K. and Lässig, J., 2016. Global versus local search: The impact of population sizes on evolutionary algorithm performance. Journal of Global Optimization, 66(3), pp.511-534. [ www ]
  • Tang, Q., Liang, Y., Zhang, L., Floudas, C. A. and Cao, X., 2016. Balancing mixed-model assembly lines with sequence-dependent tasks via hybrid genetic algorithm. Journal of Global Optimization, 65(1), pp.83-107. [ www ] ( GA )
  • Brandão, J. S., Noronha, T. F. and Ribeiro, C. C., 2016. A biased random-key genetic algorithm to maximize the number of accepted lightpaths in WDM optical networks. Journal of Global Optimization, 65(4), pp.813-835. [ www ] ( GA )
  • Ruiz, A.B., Saborido, R. and Luque, M., 2015. A preference-based evolutionary algorithm for multiobjective optimization: The weighting achievement scalarizing function genetic algorithm. Journal of Global Optimization, 62(1), pp.101-129. [ www ]
  • Yun, Y., Nakayama, H. and Yoon, M., 2016. Generation of Pareto optimal solutions using generalized DEA and PSO. Journal of Global Optimization, 64(1), pp.49-61. [ www ] ( PSO )
  • Padhye, N., Bhardawaj, P. and Deb, K., 2013. Improving differential evolution through a unified approach. Journal of Global Optimization, 55(4), pp.771-799. [ www ] ( DE )
  • Ferreiro, A.M., García, J.A., López-Salas, J.G. and Vázquez, C., 2013. An efficient implementation of parallel simulated annealing algorithm in GPUs. Journal of Global Optimization, 57(3), pp.863-890. [ www ] ( SA )
  • Yang, C. and Kumar, M., 2015. An information guided framework for simulated annealing. Journal of Global Optimization, 62(1), pp.131-154. [ www ] ( SA )
  • Bertsimas, D. and Nohadani, O., 2010. Robust optimization with simulated annealing. Journal of Global Optimization, 48(2), pp.323-334. [ www ] ( SA )
  • Chen, D.J., Lee, C.Y., Park, C.H. and Mendes, P., 2007. Parallelizing simulated annealing algorithms based on high-performance computer. Journal of Global Optimization, 39(2), pp.261-289. [ www ] ( SA )
  • Schutte, J.F. and Groenwold, A.A., 2005. A study of global optimization using particle swarms. Journal of Global Optimization, 31(1), pp.93-108. [ www ] ( PSO )
  • Onbaşoğlu, E. and Özdamar, L., 2001. Parallel simulated annealing algorithms in global optimization. Journal of Global Optimization, 19(1), pp.27-50. [ www ] ( SA )
  • Gutjahr, W.J. and Pflug, G.C., 1996. Simulated annealing for noisy cost functions. Journal of Global Optimization, 8(1), pp.1-13. [ www ] ( SA )