Journals


  1. H. Wang, A. E. Rodriguez-Fernandez, L. Uribe, A. Deutz, O. Cortés-Piña and O. Schütze, "A Newton Method for Hausdorff Approximations of the Pareto Front Within Multi-objective Evolutionary Algorithms," in IEEE Transactions on Evolutionary Computation.
    https://doi.org/10.1109/TEVC.2024.3469373

  2. A. E. Rodriguez-Fernandez, L. Schäpermeier, C. Hernández, P. Kerschke, H. Trautmann and O. Schütze, "Finding ϵ-Locally Optimal Solutions for Multi-Objective Multimodal Optimization," in IEEE Transactions on Evolutionary Computation.
    https://doi.org/10.1109/TEVC.2024.3458855

  3. Schütze, O., Rodriguez-Fernandez, A. E., Segura, C., & Hernández, C. (2024). Finding the Set of Nearly Optimal Solutions of a Multi-Objective Optimization Problem. IEEE Transactions On Evolutionary Computation.
    https://doi.org/10.1109/tevc.2024.3353546

  4. Segura, C., Chacón Castillo, J., & Schütze, O. (2023). The importance of diversity in the variable space in the design of multi-objective evolutionary algorithms. Applied Soft Computing, 136(110069), 110069.
    https://doi.org/10.1016/j.asoc.2023.110069

  5. Wang, H., Emmerich, M., Deutz, A., Hernández, V. A. S., & Schütze, O. (2023). The Hypervolume Newton Method for constrained multi-objective optimization problems. Mathematical & Computational Applications, 28(1), 10.
    https://doi.org/10.3390/mca28010010

  6. Sandoval, C., Cuate, O., González, L. C., Trujillo, L., & Schütze, O. (2022). Towards fast approximations for the hypervolume indicator for multi-objective optimization problems by Genetic Programming. Applied Soft Computing, 125, 109103.
    https://doi.org/10.1016/j.asoc.2022.109103

  7. Hernández Castellanos, C. I., & Schütze, O. (2022). A Bounded Archiver for Hausdorff Approximations of the Pareto Front for Multi-Objective Evolutionary Algorithms. Mathematical and Computational Applications, 27(3), 48.
    https://doi.org/10.3390/mca27030048

  8. Uribe, L., Lara, A., Deb, K., & Schütze, O. (2021). A new gradient free local search mechanism for constrained multi-objective optimization problems. Swarm and Evolutionary Computation, 67, 100938.
    https://doi.org/10.1016/j.swevo.2021.100938

  9. Beltrán, F., Cuate, O., & Schütze, O. (2020). The Pareto Tracer for General Inequality Constrained Multi-Objective Optimization Problems. Mathematical and Computational Applications, 25(4), 80.
    https://doi.org/10.3390/mca25040080

  10. Laredo, D., Ma, S. F., Leylaz, G., Schütze, O., & Sun, J. Q. (2020). Automatic model selection for fully connected neural networks. International Journal of Dynamics and Control, 8(4), 1063–1079.
    https://doi.org/10.1007/s40435-020-00708-w

  11. Hernández Castellanos, C. I., Schütze, O., Sun, J. Q., Morales-Luna, G., & Ober-Blöbaum, S. (2020). Numerical Computation of Lightly Multi-Objective Robust Optimal Solutions by Means of Generalized Cell Mapping. Mathematics, 8(11), 1959.
    https://doi.org/10.3390/math8111959

  12. Cuate, O., & Schütze, O. (2020). Pareto Explorer for Finding the Knee for Many Objective Optimization Problems. Mathematics, 8(10), 1651.
    https://doi.org/10.3390/math8101651

  13. Ramos-Figueroa, O., Quiroz-Castellanos, M., Mezura-Montes, E., & Schütze, O. (2020). Metaheuristics to solve grouping problems: A review and a case study. Swarm and Evolutionary Computation, 53, 100643.
    https://doi.org/10.1016/j.swevo.2019.100643

  14. Hernández Castellanos, C. I., Schütze, O., Sun, J. Q., & Ober-Blöbaum, S. (2020). Non-Epsilon Dominated Evolutionary Algorithm for the Set of Approximate Solutions. Mathematical and Computational Applications, 25(1), 3.
    https://doi.org/10.3390/mca25010003

  15. Cuate, O., Ponsich, A., Uribe, L., Zapotecas-Martínez, S., Lara, A., & Schütze, O. (2020). A New Hybrid Evolutionary Algorithm for the Treatment of Equality Constrained MOPs. Mathematics, 8(1), 7.
    https://doi.org/10.3390/math8010007

  16. Cuate, O., Uribe, L., Lara, A., & Schütze, O. (2020). A benchmark for equality constrained multi-objective optimization. Swarm and Evolutionary Computation, 52, 100619.
    https://doi.org/10.1016/j.swevo.2019.100619

  17. Cuate, O., Uribe, L., Lara, A., & Schütze, O. (2020). Dataset on a Benchmark for Equality Constrained Multi-objective Optimization. Data in Brief, 29, 105130.
    https://doi.org/10.1016/j.dib.2020.105130

  18. Uribe, L., Lara, A., & Schütze, O. (2020). On the efficient computation and use of multi-objective descent directions within constrained MOEAs. Swarm and Evolutionary Computation, 52, 100617.
    https://doi.org/10.1016/j.swevo.2019.100617

  19. Uribe, L., Bogoya, J. M., Vargas, A., Lara, A., Rudolph, G., & Schütze, O. (2020). A Set Based Newton Method for the Averaged Hausdorff Distance for Multi-Objective Reference Set Problems. Mathematics, 8(10), 1822.
    https://doi.org/10.3390/math8101822

  20. Schütze, O., Cuate, O., Martín, A., Peitz, S., & Dellnitz, M. (2020). Pareto Explorer: a global/local exploration tool for many-objective optimization problems. Engineering Optimization, 52(5), 832–855.
    https://doi.org/10.1080/0305215x.2019.1617286

  21. Sosa Hernandez, V. A., Schutze, O., Wang, H., Deutz, A., & Emmerich, M. (2020). The Set-Based Hypervolume Newton Method for Bi-Objective Optimization. IEEE Transactions on Cybernetics, 50(5), 2186–2196.
    https://doi.org/10.1109/tcyb.2018.2885974

  22. Cuate, O., & Schütze, O. (2019). Variation Rate to Maintain Diversity in Decision Space within Multi-Objective Evolutionary Algorithms. Mathematical and Computational Applications, 24(3), 82.
    https://doi.org/10.3390/mca24030082

  23. Laredo, D., Chen, Z., Schütze, O., & Sun, J. Q. (2019). A neural network-evolutionary computational framework for remaining useful life estimation of mechanical systems. Neural Networks, 116, 178–187.
    https://doi.org/10.1016/j.neunet.2019.04.016

  24. Alvarado-Iniesta, A., Cuate, O., & Schütze, O. (2019). Multi-objective and many objective design of plastic injection molding process. The International Journal of Advanced Manufacturing Technology, 102(9–12), 3165–3180.
    https://doi.org/10.1007/s00170-019-03432-8

  25. Lara, A., Uribe, L., Alvarado, S., Sosa, V. A., Wang, H., & Schütze, O. (2019). On the choice of neighborhood sampling to build effective search operators for constrained MOPs. Memetic Computing, 11(2), 155–173.
    https://doi.org/10.1007/s12293-018-0273-6

  26. Wang, H., Laredo, D., Cuate, O., & Schütze, O. (2019). Enhanced directed search: a continuation method for mixed-integer multi-objective optimization problems. Annals of Operations Research, 279(1–2), 343–365.
    https://doi.org/10.1007/s10479-018-3060-3

  27. Schütze, O., Hernández, C., Talbi, E. G., Sun, J. Q., Naranjani, Y., & Xiong, F. R. (2019). Archivers for the representation of the set of approximate solutions for MOPs. Journal of Heuristics, 25(1), 71–105.
    https://doi.org/10.1007/s10732-018-9383-z

  28. Galaviz-Aguilar, J. A., Roblin, P., Cárdenas-Valdez, J. R., Z-Flores, E., Trujillo, L., Nuñez-Pérez, J. C., & Schütze, O. (2019). Comparison of a genetic programming approach with ANFIS for power amplifier behavioral modeling and FPGA implementation. Soft Computing, 23(7), 2463–2481.
    https://doi.org/10.1007/s00500-017-2941-8

  29. Bogoya, J., Vargas, A., Cuate, O., & Schütze, O. (2018). A (p,q)-Averaged Hausdorff Distance for Arbitrary Measurable Sets. Mathematical and Computational Applications, 23(3), 51.
    https://doi.org/10.3390/mca23030051

  30. Alvarado, S., Segura, C., Schütze, O., & Zapotecas, S. (2018). The Gradient Subspace Approximation as Local Search Engine within Evolutionary Multi-objective Optimization Algorithms. Computación y Sistemas, 22(2).
    https://doi.org/10.13053/cys-22-2-2948

  31. Martín, A., & Schütze, O. (2018). Pareto Trace612–621. https:/doi.org/10.1524/auto.2012.1033r: a predictor–corrector method for multi-objective optimization problems. Engineering Optimization, 50(3), 516–536.
    https://doi.org/10.1080/0305215x.2017.1327579

  32. Uribe, L., Perea, B., Hernández-del-Valle, G., & Schütze, O. (2018). A Hybrid Metaheuristic for the Efficient Solution of GARCH with Trend Models. Computational Economics, 52(1), 145–166.
    https://doi.org/10.1007/s10614-017-9666-8

  33. Naranjani, Y., Hernández, C., Xiong, F. R., Schütze, O., & Sun, J. Q. (2017). A hybrid method of evolutionary algorithm and simple cell mapping for multi-objective optimization problems. International Journal of Dynamics and Control, 5(3), 570–582.
    https://doi.org/10.1007/s40435-016-0250-1

  34. Dibene, J. C., Maldonado, Y., Vera, C., de Oliveira, M., Trujillo, L., & Schütze, O. (2017). Optimizing the location of ambulances in Tijuana, Mexico. Computers in Biology and Medicine, 80, 107–115.
    https://doi.org/10.1016/j.compbiomed.2016.11.016
    Honors status (received in May 2018).

  35. Schütze, O., Alvarado, S., Segura, C., & Landa, R. (2017). Gradient subspace approximation: a direct search method for memetic computing. Soft Computing, 21(21), 6331–6350.
    https://doi.org/10.1007/s00500-016-2187-x

  36. Sardahi, Y., Sun, J. Q., Hernández, C., & Schütze, O. (2017). Many-Objective Optimal and Robust Design of Proportional-Integral-Derivative Controls With a State Observer. Journal of Dynamic Systems, Measurement, and Control, 139(2).
    https://doi.org/10.1115/1.4034749

  37. Schütze, O., Domínguez-Medina, C., Cruz-Cortés, N., Gerardo De La Fraga, L., Sun, J. Q., Toscano, G., & Landa, R. (2016). A scalar optimization approach for averaged Hausdorff approximations of the Pareto front. Engineering Optimization, 48(9), 1593–1617.
    https://doi.org/10.1080/0305215x.2015.1124872

  38. Hernández Mejía, J. A., Schütze, O., Cuate, O., Lara, A., & Deb, K. (2016). RDS-NSGA-II: a memetic algorithm for reference point based multi-objective optimization. Engineering Optimization, 49(5), 828–845.
    https://doi.org/10.1080/0305215x.2016.1211127

  39. Fernández, J., Schütze, O., Hernández, C., Sun, J. Q., & Xiong, F. R. (2016). Parallel simple cell mapping for multi-objective optimization. Engineering Optimization, 48(11), 1845–1868.
    https://doi.org/10.1080/0305215x.2016.1145215

  40. Rudolph, G., Schütze, O., Grimme, C., Domínguez-Medina, C., & Trautmann, H. (2016). Optimal averaged Hausdorff archives for bi-objective problems: theoretical and numerical results. Computational Optimization and Applications, 64(2), 589–618.
    https://doi.org/10.1007/s10589-015-9815-8

  41. Pérez, N., Cuate, O., Schütze, O., & Alvarado, A. (2016). Including Users Preferences in the Decision Making for Discrete Many Objective Optimization Problems. Computación y Sistemas, 20(4).
    https://doi.org/10.13053/cys-20-4-2501

  42. Xiong, F. R., Schütze, O., Ding, Q., & Sun, J. Q. (2016). Finding zeros of nonlinear functions using the hybrid parallel cell mapping method. Communications in Nonlinear Science and Numerical Simulation, 34, 23–37.
    https://doi.org/10.1016/j.cnsns.2015.10.008

  43. Schütze, O., Hernández, V. A. S., Trautmann, H., & Rudolph, G. (2016). The hypervolume based directed search method for multi-objective optimization problems. Journal of Heuristics, 22(3), 273–300.
    https://doi.org/10.1007/s10732-016-9310-0

  44. Xiong, F. R., Qin, Z. C., Ding, Q., Hernández, C., Fernandez, J., Schütze, O., & Sun, J. Q. (2015). Parallel Cell Mapping Method for Global Analysis of High-Dimensional Nonlinear Dynamical Systems1. Journal of Applied Mechanics, 82(11).
    https://doi.org/10.1115/1.4031149

  45. Qin, Z. C., Xiong, F. R., Ding, Q., Hernández, C., Fernandez, J., Schütze, O., & Sun, J. Q. (2015). Multi-objective optimal design of sliding mode control with parallel simple cell mapping method. Journal of Vibration and Control, 23(1), 46–54.
    https://doi.org/10.1177/1077546315574948

  46. Xiong, F. R., Qin, Z. C., Xue, Y., Schütze, O., Ding, Q., & Sun, J. Q. (2014). Multi-objective optimal design of feedback controls for dynamical systems with hybrid simple cell mapping algorithm. Communications in Nonlinear Science and Numerical Simulation, 19(5), 1465–1473.
    https://doi.org/10.1016/j.cnsns.2013.09.032

  47. Hernández, C., Naranjani, Y., Sardahi, Y., Liang, W., Schütze, O., & Sun, J. Q. (2013). Simple cell mapping method for multi-objective optimal feedback control design. International Journal of Dynamics and Control, 1(3), 231–238.
    https://doi.org/10.1007/s40435-013-0021-1

  48. Xiong, F., Qin, Z., Hernández, C., Sardahi, Y., Narajani, Y., Liang, W., Xue, Y., Schütze, O., & Sun, J. (2013). A multi-objective optimal PID control for a nonlinear system with time delay. Theoretical and Applied Mechanics Letters, 3(6), 063006.
    https://doi.org/10.1063/2.1306306

  49. Rudolph, G., Trautmann, H., & Schütze, O. (2012). Homogene Approximation der Paretofront bei mehrkriteriellen Kontrollproblemen. auto, 60(10), 612–621.
    https://doi.org/10.1524/auto.2012.1033

  50. Ringkamp, M., Ober-Blöbaum, S., Dellnitz, M., & Schütze, O. (2012). Handling high-dimensional problems with multi-objective continuation methods via successive approximation of the tangent space. Engineering Optimization, 44(9), 1117–1146.
    https://doi.org/10.1080/0305215x.2011.634407

  51. Schutze, O., Esquivel, X., Lara, A., & Coello, C. A. C. (2012). Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 16(4), 504–522.
    https://doi.org/10.1109/tevc.2011.2161872
    Paper Award (bestowed in 2015).

  52. Avigad, G., Eisenstadt, E., & Schuetze, O. (2011). Handling changes of performance requirements in multi-objective problems. Journal of Engineering Design, 23(8), 597–617.
    https://doi.org/10.1080/09544828.2011.630656

  53. Schütze, O., Lara, A., Coello, C. A. C., & Vasile, M. (2011). On the detection of nearly optimal solutions in the context of single-objective space mission design problems. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 225(11), 1229–1242.
    https://doi.org/10.1177/0954410011413693

  54. Schutze, O., Lara, A., & Coello, C. A. C. (2011). On the Influence of the Number of Objectives on the Hardness of a Multiobjective Optimization Problem. IEEE Transactions on Evolutionary Computation, 15(4), 444–455.
    https://doi.org/10.1109/tevc.2010.2064321

  55. Schutze, O., Vasile, M., & Coello, C. A. C. (2011). Computing the Set of Epsilon-Efficient Solutions in Multiobjective Space Mission Design. Journal of Aerospace Computing, Information, and Communication, 8(3), 53–70.
    https://doi.org/10.2514/1.46478

  56. Schütze, O., Laumanns, M., Tantar, E., Coello, C. A. C., & Talbi, E. G. (2010). Computing Gap Free Pareto Front Approximations with Stochastic Search Algorithms. Evolutionary Computation, 18(1), 65–96.
    https://doi.org/10.1162/evco.2010.18.1.18103

  57. Lara, A., Sanchez, G., Coello Coello, C., & Schutze, O. (2010). HCS: A New Local Search Strategy for Memetic Multiobjective Evolutionary Algorithms. IEEE Transactions on Evolutionary Computation, 14(1), 112–132.
    https://doi.org/10.1109/tevc.2009.2024143
    Paper Award (bestowed in 2013).

  58. Dellnitz, M., Ober-Blöbaum, S., Post, M., Schütze, O., & Thiere, B. (2009). A multi-objective approach to the design of low thrust space trajectories using optimal control. Celestial Mechanics and Dynamical Astronomy, 105(1–3), 33–59.
    https://doi.org/10.1007/s10569-009-9229-y

  59. Jourdan, L., Schütze, O., Legrand, T., Talbi, E. G., & Wojkiewicz, J. L. (2009). An Analysis of the Effect of Multiple Layers in the Multi-Objective Design of Conducting Polymer Composites. Materials and Manufacturing Processes, 24(3), 350–357.
    https://doi.org/10.1080/10426910802679535

  60. Schütze, O., Vasile, M., Junge, O., Dellnitz, M., & Izzo, D. (2009). Designing optimal low-thrust gravity-assist trajectories using space pruning and a multi-objective approach. Engineering Optimization, 41(2), 155–181.
    https://doi.org/10.1080/03052150802391734

  61. de la Fraga, L. G., & Schütze, O. (2008). Direct Calibration by Fitting of Cuboids to a Single Image Using Differential Evolution. International Journal of Computer Vision, 81(2), 119–127.
    https://doi.org/10.1007/s11263-008-0183-

  62. Schütze, O., Laumanns, M., Coello Coello, C. A., Dellnitz, M., & Talbi, E. G. (2008). Convergence of stochastic search algorithms to finite size pareto set approximations. Journal of Global Optimization, 41(4), 559–577.
    https://doi.org/10.1007/s10898-007-9265-7

  63. Schütze, O., Jourdan, L., Legrand, T., Talbi, E. G., & Wojkiewicz, J. L. (2008). New analysis of the optimization of electromagnetic shielding properties using conducting polymers and a multi-objective approach. Polymers for Advanced Technologies, 19(7), 762–769.
    https://doi.org/10.1002/pat.1030

  64. Schütze, O., Coello Coello, C. A., Mostaghim, S., Talbi, E. G., & Dellnitz, M. (2008). Hybridizing evolutionary strategies with continuation methods for solving multi-objective problems. Engineering Optimization, 40(5), 383–402.
    https://doi.org/10.1080/03052150701821328

  65. Dellnitz, M., Schütze, O., & Hestermeyer, T. (2005). Covering Pareto Sets by Multilevel Subdivision Techniques. Journal of Optimization Theory and Applications, 124(1), 113–136.
    https://doi.org/10.1007/s10957-004-6468-7

  66. Schütze, O. (2003). A New Data Structure for the Nondominance Problem in Multi-objective Optimization. Lecture Notes in Computer Science, 509–518.
    https://doi.org/10.1007/3-540-36970-8_36

  67. Dellnitz, M., Schütze, O., & Zheng, Q. (2002). Locating all the zeros of an analytic function in one complex variable. Journal of Computational and Applied Mathematics, 138(2), 325–333.
    https://doi.org/10.1016/s0377-0427(01)00371-5

  68. Dellnitz, M., Schütze, O., & Sertl, S. (2002). Finding zeros by multilevel subdivision techniques. IMA Journal of Numerical Analysis, 22(2), 167–185.
    https://doi.org/10.1093/imanum/22.2.167