PGA/MOEAD: a preference-guided evolutionary algorithm for multi-objective decision-making problems with interval-valued fuzzy preferences

References

• Branke, J., Kauler, T., & Schmeck, H. (2001). Guidance in evolutionary multi-objective optimization. Advances in Engineering Software, 32, 499–507.
   Web of Science ®Google Scholar
• Branke, J., & Deb, K. (2004). Integrating user preferences into evolutionary multi-objective optimization. KanGAL report. Kanpur: Indian Institute of Technology.
   Google Scholar
• Cheng, H. W. (2013). A satisficing method for fuzzy goal programming problems with different importance and priorities. Quality and Quantity, 47, 485–498.
   Web of Science ®Google Scholar
• Coello Coello, C. A. (2000). Handling preferences in evolutionary multiobjective optimization: A survey. In 2000 Congress on evolutionary computation (pp. 30–37). Piscataway, NJ: IEEE Service Center.
   Google Scholar
• Dai, C., & Wang, Y. (2015). A new decomposition based evolutionary algorithm with uniform designs for many-objective optimization. Applied Soft Computing, 30, 238–248.
   Web of Science ®Google Scholar
• Deb, K. (1999). Multi-objective evolutionary algorithms: Introducing bias among Pareto-optimal solutions. KanGAL report. Kanpur: Indian Institute of Technology.
   Google Scholar
• Deb, K., Thiele, L., Laumanns, M., & Zitzler, E. (2002). Scalable multi-objective optimization test problems. In Proceedings of the congress on Evolutionary Computation (pp. 825–830). 1.
   Google Scholar
• Deb, K., Sundar, J., Uday, B. R. N., & Chaudhuri, S. (2006). Reference point based multi-objective optimization using evolutionary algorithms. Conference on Genetic & Evolutionary Computation, 2, 273–286.
   Google Scholar
• Dragan, C., & Parmee, I. (2002). Designer's preferences and multi-objective preliminary design processes. London: Springer.
   Google Scholar
• Fonseca, C. M., & Fleming, P. J. (1995). Multiobjective evolutionary algorithms made easy: Selection, sharing, and mating restriction. In Proceedings of the First International Conference on Evolutionary Algorithms in Engineering Systems: Innovations and Applications, Sheffield, UK.
   Google Scholar
• Giagkiozis, I., & Fleming, P. (2014). Pareto front estimation for decision making. Evolutionary Computation, 22, 651–678.
   PubMed Web of Science ®Google Scholar
• Gong, D., Ji, X., Sun, J., & Sun, X. (2012). Interactive evolutionary algorithms with decision-maker's preferences for solving interval multi-objective optimization problems. Neurocomputing, 137(4), 241–251.
   Google Scholar
• Gopalan, R. (2014). The aircraft maintenance base location problem. European Journal of Operational Research, 236, 634–642.
   Web of Science ®Google Scholar
• Goulart, F., & Campelo, F. (2016). Preference-guided evolutionary algorithms for many-objective optimization. Information Sciences, 329, 236–255.
   Web of Science ®Google Scholar
• Greenwood, G. W., Hu, X., & D'Ambrosio, J. G. (1996). Fitness function for multiple objective optimization problems: Combining preferences with Pareto ranking. In Foundations of genetic algorithms. (pp. 437–455), San Francisco, CA: Morgan Kaufmann.
   Google Scholar
• Hu, J., Zhang, X., Chen, X., & Liu, Y. (2015). Hesitant fuzzy information measures and their applications in multi-criteria decision making. International Journal of Systems Science, 47(1), 1–15.
   Web of Science ®Google Scholar
• Karahan, İ., & Köksalan, M. (2010). A territory defining multiobjective evolutionary algorithms and preference incorporation. IEEE Transactions on Evolutionary Computation, 14, 636–664.
   Web of Science ®Google Scholar
• Klamroth, K., & Miettinen, K. (2008). Integrating approximation and interactive decision making in multicriteria optimization. Operation Research, 56, 222–234.
   Web of Science ®Google Scholar
• Kumar, D. D. P. (2013). Application of fuzzy goal programming approach to multi-objective linear fractional inventory model. International Journal of Systems Science, 46(12), 1–10.
   Web of Science ®Google Scholar
• Kӧksalan, M., Wallenius, J., & Zoints, S. (2011). Multiple criteria decision making: From early history to the 21st century. Singapore, SG: World Scientific Publishing.
   Google Scholar
• Köksalan, M., & Karakaya, G. (2014). An evolutionary algorithm for finding efficient solutions in multi-attribute auctions. International Journal of Information Technology and Decision Making, 13, 649–673.
   Web of Science ®Google Scholar
• Lewis, G. H., Srinivasan, A., & Subrahmanian, E. (1998). Staffing and allocation of workers in an administrative office. Management Science, 44, 548–570.
   Web of Science ®Google Scholar
• Li, H., & Zhang, Q. (2009). Multi-objective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II. IEEE Transactions on Evolutionary Computation, 13, 284–302.
   Web of Science ®Google Scholar
• Liao, H. C., Xu, Z. S., & Xia, M. M. (2014). Multiplicative consistency of hesitant fuzzy preference relation and its application in group decision making. International Journal of Information Technology and Decision Making, 13, 47–76.
   Web of Science ®Google Scholar
• Lipscomb, J., Parmigiani, G., & Hasselblad, V. (1998). Combining expert judgment by hierarchical modeling: An application to physician staffing. Management Science, 44, 149–161.
   Web of Science ®Google Scholar
• Messac, A., Ismail-Yahaya, A., & Mattson, C. (2003). The normalized normal constraint method for generating the Pareto frontier. Structural and Multidisciplinary Optimization, 25, 86–98.
   Web of Science ®Google Scholar
• Öztürk, D. T., & Köksalan, M. (2016). An interactive approach for biobjective integer programs under quasiconvex preference functions. Annals of Operations Research, 244, 1–20.
   Web of Science ®Google Scholar
• Phelps, S., & Koksalan, M. (2003). An interactive evolutionary metaheuristic for multiobjective combinatorial optimization. Management Science, 49, 1726–1738.
   Web of Science ®Google Scholar
• Pierro, F. D., Shoon-Thiam, K., & Savic, D. A. (2007). An investigation on preference order ranking scheme for multiobjective evolutionary optimization. IEEE Transactions on Evolutionary Computation, 11, 17–45.
   Web of Science ®Google Scholar
• Pointing, L., & Lan, L. (2015). Tomorrow's hanger. Aviation Maintenance and Engineering, 9, 39–42.
   Google Scholar
• Sarjaš, A., Chowdhury, A., & Svečko, R. (2015). Multi-criteria optimal pole assignment robust controller design for uncertainty systems using an evolutionary algorithm. International Journal of Systems Science, 47(12), 1–16.
   Web of Science ®Google Scholar
• Thiele, L., Miettinen, K., Korhonen, P. J., & Molina, J. (2009). A preference-based evolutionary algorithm for multi-objective optimization. Evolutionary Computation, 17(3), 411–436.
   PubMed Web of Science ®Google Scholar
• Tian, Z., Zhang, H., Wang, J., Wang, J., & Chen, X. (2016). Multi-criteria decision-making method based on a cross-entropy with interval neutrosophic sets. International Journal of Systems Science, 47(15), 3595–3608.
   Web of Science ®Google Scholar
• Wang, R., Mansor, M. M., Purshouse, R. C., & Fleming, P. J. (2015). An analysis of parameter sensitivities of preference-inspired co-evolutionary algorithms. International Journal of Systems Science, 46(13), 1–14.
   Web of Science ®Google Scholar
• Wang, P., Zhu, Z., & Huang, S. (2017). The use of improved TOPSIS method based on experimental design and Chebyshev regression in solving MCDM problems. Journal of Intelligent Manufacturing, 28, 1–15.
   Web of Science ®Google Scholar
• Xu, Z., & Cai, X. (2011). Group consensus algorithms based on preference relations. Information Sciences, 181, 150–162.
   Web of Science ®Google Scholar
• Zhang, Q. F., & Li, H. (2007). MOEA/D: A multi-objective evolutionary algorithm based on decomposition. IEEE Transaction on Evolutionary Computation, 6, 712–731.
   Web of Science ®Google Scholar
• Zhu, Y., & Luo, Y. (2015). Multi-objective optimization and decision-making of space station logistics strategies. International Journal of Systems Science, 47(13), 1–17.
   Web of Science ®Google Scholar
• Zitzler, E., & Thiele, L. (1999). Multi-objective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3, 257–271.
   Web of Science ®Google Scholar
• Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multi-objective evolutionary algorithms: Empirical results. Evolutionary Computation, 8, 173–195.
   PubMed Web of Science ®Google Scholar
• Zio, E., Baraldi, P., & Pedroni, N. (2009). Optimal power system generation scheduling by multi-objective genetic algorithms with preferences. Reliability Engineering and System Safety, 94, 432–444.
   Web of Science ®Google Scholar