SpringerLink
Log in

Key Issues in Real-World Applications of Many-Objective Optimisation and Decision Analysis

  • Chapter
  • First Online:
Many-Criteria Optimization and Decision Analysis

Part of the book series: Natural Computing Series ((NCS))

Abstract

The insights and benefits to be realised through the optimisation of multiple independent, but conflicting objectives are well recognised by practitioners seeking effective and robust solutions to real-world application problems. Key issues encountered by users of many-objective optimisation (>3 objectives) in a real-world environment are discussed here. These include how to formulate the problem and develop a suitable decision-making framework, together with considering different ways in which decision-makers may be involved. Ways to manage the reduction of computational load and how to reduce the sensitivity of candidate solutions as a result of the inevitable uncertainties that arise in real-world applications are addressed. Other state-of-the-art topics such as the use of machine learning and the management of complex issues arising from multidisciplinary applications are also examined. It is recognised that optimisation in real-world applications is commonly undertaken by users and decision-makers who need not have specialist expertise in many-objective optimisation decision analysis methods. Advice is offered to experts and non-experts alike.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (France)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 117.69
Price includes VAT (France)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
EUR 158.24
Price includes VAT (France)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    It should be noted that the term many-objective optimisation is used by the evolutionary community. In the multiple criteria decision-making field, the distinction is typically made between bi-objective problems (with two objectives) and the rest, since the performance of the methods is not as dependent on the number of objectives as in the evolutionary approaches. Therefore, when discussing non-evolutionary methods in this chapter, the term multi-objective optimisation is used, and it does not indicate that the number of objective functions is limited to 3.

  2. 2.

    This exercise was undertaken with financial support from EPSRC, United Kingdom, and Jaguar Land Rover as part of the jointly funded Programme for Simulation Innovation (PSi) (EP/L025760/1). A publication on this particular application exercise is currently being prepared.

References

  1. P. Aghaei Pour, T. Rodemann, J. Hakanen, K. Miettinen, Surrogate assisted interactive multiobjective optimization in energy system design of buildings. Optim. Eng. 23, 303–327 (2022)

    Google Scholar 

  2. R. Allmendinger, M.T.M. Emmerich, J. Hakanen, Y. **, E. Rigoni, Surrogate-assisted multicriteria optimization: complexities, prospective solutions, and business case. J. Multi-Criteria Decis. Anal. 24(1–2), 5–24 (2017)

    Article  Google Scholar 

  3. R. Allmendinger, J. Handl, J.D. Knowles, Multiobjective optimization: when objectives exhibit non-uniform latencies. Eur. J. Oper. Res. 243(2), 497–513 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  4. R. Allmendinger, J.D. Knowles, On handling ephemeral resource constraints in evolutionary search. Evol. Comput. 21(3), 497–531 (2013)

    Article  Google Scholar 

  5. L.M. Antonio, C.A.C. Coello, Coevolutionary multiobjective evolutionary algorithms: survey of the state-of-the-art. IEEE Trans. Evol. Comput. 22(6), 851–865 (2018)

    Article  Google Scholar 

  6. D. Brockhoff, E. Zitzler, Objective reduction in evolutionary multiobjective optimization: theory and applications. Evol. Comput. 17(2), 135–166 (2009)

    Article  Google Scholar 

  7. R. Chen, K. Li, X. Yao, Dynamic multiobjectives optimization with a changing number of objectives. IEEE Trans. Evol. Comput. 22(1), 157–171 (2018)

    Article  Google Scholar 

  8. R. Cheng, C. He, Y. **, X. Yao, Model-based evolutionary algorithms: a short survey. Compl. & Intell. Syst. 4, 283–292 (2018)

    Article  Google Scholar 

  9. R. Cheng, Y. **, A competitive swarm optimizer for large scale optimization. IEEE Trans. Cybern. 45(2), 191–205 (2015)

    Article  Google Scholar 

  10. R. Cheng, Y. **, K. Narukawa, B. Sendhoff, A multiobjective evolutionary algorithm using Gaussian process based inverse modeling. IEEE Trans. Evol. Comput. 19(6), 761–856 (2015)

    Article  Google Scholar 

  11. R. Cheng, Y. **, M. Olhofer, B. Sendhoff, A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 20(5), 773–791 (2016)

    Article  Google Scholar 

  12. T. Chugh, R. Allmendinger, V. Ojalehto, K. Miettinen, Surrogate-assisted evolutionary biobjective optimization for objectives with non-uniform latencies, in Genetic and Evolutionary Computation Conference (GECCO) (ACM Press, 2018), pp. 609–616

    Google Scholar 

  13. T. Chugh, Y. **, K. Miettinen, J. Hakanen, K. Sindhya, A surrogate-assisted reference vector guided evolutionary algorithm for computationally expensive many-objective optimization. IEEE Trans. Evol. Comput. 22(1), 129–142 (2018)

    Article  Google Scholar 

  14. T. Chugh, T. Kratky, K. Miettinen, Y. **, P. Makkonen, Multiobjective shape design in a ventilation system with a preference-driven surrogate-assisted evolutionary algorithm, in Genetic and Evolutionary Computation Conference (GECCO) (ACM Press, 2019), pp. 1147–1155

    Google Scholar 

  15. T. Chugh, K. Sindhya, J. Hakanen, K. Miettinen, A survey on handling computationally expensive multiobjective optimization problems with evolutionary algorithms. Soft. Comput. 23, 3137–3166 (2019)

    Article  Google Scholar 

  16. T. Chugh, K. Sindhya, K. Miettinen, Y. **, T. Kratky, P. Makkonen, Surrogate-assisted evolutionary multiobjective shape optimization of an air intake ventilation system, in Congress on Evolutionary Computation (CEC) (IEEE Press, 2017), pp. 1541–1548

    Google Scholar 

  17. C.A. Coello, G.B. Lamont, D.A. Van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems (Springer, Berlin, 2007)

    Google Scholar 

  18. G. Critchfield, K. Willard, D. Connely, Probabilistic sensitivity analysis methods for general decision models. Comput. Biomed. Res. 19, 254–265 (1986)

    Article  Google Scholar 

  19. T.R. Cruse, Reliability-based Mechanical Design (Marcel Dekker, New York, 1997)

    Google Scholar 

  20. D. Daum, K. Deb, J. Branke, Reliability-based optimization for multiple constraint with evolutionary algorithms, in Congress on Evolutionary Computation (CEC) (IEEE Press, 2007), pp. 911–918

    Google Scholar 

  21. A.R.R. de Freitas, P.J. Fleming, F.G. Guimarães, Aggregation trees for visualization and dimension reduction in many-objective optimization. Inf. Sci. 298, 288–314 (2015)

    Article  Google Scholar 

  22. K. Deb, S. Gupta, D. Daum, J. Branke, A. Mall, D. Padmanabhan, Reliability-based optimization using evolutionary algorithms. IEEE Trans. Evol. Comput. 13(5), 1054–1074 (2009)

    Article  Google Scholar 

  23. K. Deb, H. Jain, An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: Solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577–601 (2014)

    Article  Google Scholar 

  24. K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  25. K. Deb, U.B. Rao, K. Sindhya, Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling bi-objective optimization problems, in Evolutionary Multi-criterion Optimization (EMO) (2007), pp. 803–817

    Google Scholar 

  26. K. Deb, P.C. Roy, R. Hussein, Surrogate modeling approaches for multiobjective optimization: methods, taxonomy, and results. Math. Comput. Appl. 26(1), 5 (2021)

    Google Scholar 

  27. K. Deb, A. Sinha, An efficient and accurate solution methodology for bilevel multi-objective programming problems using a hybrid evolutionary-local-search algorithm. Evol. Comput. 18(3), 403–449 (2010)

    Article  Google Scholar 

  28. K. Deb, P. Zope, A. Jain, Distributed computing of Pareto-optimal solutions using multi-objective evolutionary algorithms, in Evolutionary Multi-criterion Optimization (EMO) (Springer, Berlin, 2003), pp. 535–549

    Google Scholar 

  29. J. Ding, C. Yang, Y. **, T. Chai, Generalized multi-tasking for evolutionary optimization of expensive problems. IEEE Trans. Evol. Comput. 23(1), 44–58 (2019)

    Article  Google Scholar 

  30. W. Du, W. Zhong, Y. Tang, W. Du, Y. **, High-dimensional robust multi-objective optimization for order scheduling: a decision variable classification approach. IEEE Trans. Ind. Inf. 15(1), 293–304 (2019)

    Article  Google Scholar 

  31. J.A. Duro, Y. Yan, I. Giagkiozis, S. Giagkiozis, S. Salomon, D.C. Oara, A.K. Sriwastava, J. Morison, C.M. Freeman, R.J. Lygoe, R.C. Purshouse, P.J. Fleming, Liger: a cross-platform open-source integrated optimization and decision-making environment. Appl. Soft Comput. 98, 106851 (2021)

    Article  Google Scholar 

  32. P. Eskelinen, K. Miettinen, K. Klamroth, J. Hakanen, Pareto Navigator for interactive nonlinear multiobjective optimization. OR Spectrum 23, 211–227 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  33. M. Farina, K. Deb, P. Amato, Dynamic multiobjective optimization problems: test cases, approximations, and applications. IEEE Trans. Evol. Comput. 8(5), 425–442 (2000)

    Article  MATH  Google Scholar 

  34. H.R. Fazeley, H. Taei, H. Naseh, A multi-objective, multidisciplinary design optimization methodology for the conceptual design of a spacecraft bi-propellant propulsion system. Struct. Multidiscip. Optim. 53, 145–160 (2016)

    Article  MathSciNet  Google Scholar 

  35. J. Fieldsend, T. Chugh, R. Allmendinger, K. Miettinen, A visualizable test problem generator for many-objective optimization, IEEE Trans. Evol. Comput. 26(1), 1–11 (2022)

    Google Scholar 

  36. P.J. Fleming, R.C. Purshouse, R.J. Lygoe, Many-objective optimization: an engineering design perspective, in Evolutionary Multi-criterion Optimization (EMO) (2005), pp. 14–32

    Google Scholar 

  37. C.M. Fonseca, P.J. Fleming, Genetic algorithms for multiobjective optimization: formulation, discussion and generalization, in International Conference on Genetic Algorithms (ICGA) (Morgan Kaufmann, 1993), pp. 416–423

    Google Scholar 

  38. C.M. Fonseca, P.J. Fleming, Multiobjective optimization and multiple constraint handling with evolutionary algorithms (I): a unified formulation. IEEE Trans. Syst. Man Cybern. - Part A 28(1), 26–37 (1998)

    Article  Google Scholar 

  39. C.M. Fonseca, P.J. Fleming, Multiobjective optimization and multiple constraint handling with evolutionary algorithms (II): application example. IEEE Trans. Syst. Man Cybern. - Part A 28(1), 38–44 (1998)

    Article  Google Scholar 

  40. B. Fritzke, A growing neural gas network learns topologies, in Neural Information Processing Systems (NIPS) (MIT Press, 1995), pp. 625–632

    Google Scholar 

  41. G. Fu, C. Sun, Y. Tan, G. Zhang, Y. **, A surrogate-assisted evolutionary algorithm with random feature selection for large-scale expensive problems, in Parallel Problem Solving from Nature (PPSN) (Springer, 2020), pp. 125–139

    Google Scholar 

  42. A. Gaur, A.K. Talukder, K. Deb, S. Tiwari, S. Xu, D. Jones, Unconventional optimization for achieving well-informed design solutions for the automobile industry. Eng. Optim. 52(9), 1542–1560 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  43. V.J. Gillet, W. Khatib, P. Willett, P.J. Fleming, D.V.S. Green, Combinatorial library design using a multiobjective genetic algorithm. J. Chem. Inf. Comput. Sci. 42(2), 375–385 (2002)

    Article  Google Scholar 

  44. I. Goodfellow, Y. Bengio, A. Courville, F. Bach, Deep Learning (MIT Press, 2017)

    Google Scholar 

  45. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, in Neural Information Processing Systems (NIPS) (2014), pp. 2672–2680

    Google Scholar 

  46. L. Graening, B. Sendhoff, Shape mining: a holistic data mining approach for engineering design. Adv. Eng. Inf. 28, 166–185 (2014)

    Article  Google Scholar 

  47. D. Guo, Y. **, J. Ding, T. Chai, Heterogeneous ensemble-based infill criterion for evolutionary multiobjective optimization of expensive problems. IEEE Trans. Cybern. 49(3), 1012–1025 (2019)

    Article  Google Scholar 

  48. D. Guo, X. Wang, K. Gao, Y. **, J. Ding, T. Chai, Evolutionary optimization of high-dimensional multi- and many-objective expensive problems assisted by a dropout neural network. IEEE Trans. Syst. Man Cybern.: Syst. 52(4), 2084–2097 (2020)

    Article  Google Scholar 

  49. A. Gupta, Y.S. Ong, L. Feng, Multifactorial evolution: toward evolutionary multitasking. IEEE Trans. Evol. Comput. 20(3), 343–357 (2016)

    Article  Google Scholar 

  50. A. Gupta, Y.S. Ong, L. Feng, Insights on transfer optimization: because experience is the best teacher. IEEE Trans. Emerg. Topics Comput. Intell. 2(1), 51–64 (2018)

    Article  Google Scholar 

  51. A. Habib, H.K. Singh, T. Chugh, T. Ray, K. Miettinen, A multiple surrogate assisted decomposition-based evolutionary algorithm for expensive multi/many-objective optimization. IEEE Trans. Evol. Comput. 23(6), 1000–1014 (2019)

    Article  Google Scholar 

  52. D. Hadka, P.M. Reed, Borg: An auto-adaptive many-objective evolutionary computing framework. Evol. Comput. 21(2), 231–259 (2013)

    Article  Google Scholar 

  53. J. Hakanen, K. Miettinen, K. Sahlstedt, Wastewater treatment: New insight provided by interactive multiobjective optimization. Decis. Support Syst. 51, 328–337 (2011)

    Article  Google Scholar 

  54. J. Hakanen, K. Sahlstedt, K. Miettinen, Wastewater treatment plant design and operation under multiple conflicting objective functions. Environ. Model. Softw. 46(1), 240–249 (2013)

    Article  Google Scholar 

  55. J. Hämäläinen, K. Miettinen, P. Tarvainen, J. Toivanen, Interactive solution approach to a multiobjective optimization problem in paper machine headbox design. J. Optim. Theory Appl. 116(2), 265–281 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  56. M. Hartikainen, K. Miettinen, K. Klamroth, Interactive Nonconvex Pareto Navigator for multiobjective optimization. Eur. J. Oper. Res. 275(1), 238–251 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  57. M. Hartikainen, K. Miettinen, M. Wiecek, PAINT: Pareto front interpolation for nonlinear multiobjective optimization. Comput. Optim. Appl. 52(3), 845–867 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  58. M. Hartikainen, V. Ojalehto, K. Sahlstedt, Applying the approximation method PAINT and the interactive method NIMBUS to the multiobjective optimization of operating a wastewater treatment plant. Eng. Optim. 47(3), 328–346 (2015)

    Article  Google Scholar 

  59. C. He, S. Huang, R. Cheng, K.C. Tan, Y. **, Evolutionary multiobjective optimization driven by generative adversarial networks (GANs). IEEE Trans. Cybern. 51(6), 3129–3142 (2020)

    Article  Google Scholar 

  60. Y. Hua, Y. **, K. Hao, A clustering based adaptive evolutionary algorithm for multi-objective optimization with irregular Pareto fronts. IEEE Trans. Cybern. 49(7), 2758–2770 (2019)

    Article  Google Scholar 

  61. Y. Hua, Q. Liu, K. Hao, Y. **, A survey of evolutionary algorithms for multi-objective optimization problems with irregular Pareto fronts. IEEE/CAA J. Autom. Sinica 8(2), 303–318 (2021)

    Article  MathSciNet  Google Scholar 

  62. C.-L. Hwang, A.S.M. Masud, Multiple Objective Decision Making – Methods and Applications (Springer, 1979)

    Google Scholar 

  63. J. Ide, A. Schöbel, Robustness for uncertain multi-objective optimization: a survey and analysis of different concepts. OR Spectrum 38(1), 235–271 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  64. H. Jain, K. Deb, An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part II: Handling constraints and extending to an adaptive approach. IEEE Trans. Evol. Comput. 18(4), 602–622 (2014)

    Article  Google Scholar 

  65. A. Jakulin, I. Bratko, Testing the significance of attribute interactions, in International Conference on Machine Learning (ICML) (ACM Press, 2004), pp. 409–416

    Google Scholar 

  66. G. James, D. Witten, T. Hastie, R. Tibshirani, An Introduction to Statistical Learning (Springer, 2013)

    Google Scholar 

  67. J.-R. Jian, Z.-H. Zhan, J. Zhang, Large-scale evolutionary optimization: a survey and experimental comparative study. Int. J. Mach. Learn. Cybern. 11, 729–745 (2020)

    Article  Google Scholar 

  68. M. Jiang, Z. Huang, L. Qiu, W. Huang, G.G. Yen, Transfer learning-based dynamic multiobjective optimization algorithms. IEEE Trans. Evol. Comput. 22(4), 501–514 (2018)

    Article  Google Scholar 

  69. C.D. Jilla, D.W. Miller, Multi-objective, multidisciplinary design optimization methodology for distributed satellite systems. J. Spacecr. Rocket. 41(1), 39–50 (2004)

    Article  Google Scholar 

  70. Y. **, H. Wang, T. Chugh, D. Guo, K. Miettinen, Data-driven evolutionary optimization: an overview and case studies. IEEE Trans. Evol. Comput. 23(3), 442–458 (2019)

    Article  Google Scholar 

  71. D.R. Jones, M. Schonlau, W.J. Welch, Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4), 455–492 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  72. D. Kahneman, A. Tversky, Prospect theory: an analysis of decision under risk. Econometrica 47(2), 263–291 (1979)

    Article  MathSciNet  MATH  Google Scholar 

  73. K. Klamroth, S. Mostaghim, B. Naujoks, S. Poles, R. Purshouse, G. Rudolph, S. Ruzika, S. Sayın, M.M. Wiecek, X. Yao, Multiobjective optimization for interwoven systems. J. Multi-Criteria Decis. Anal. 24, 71–81 (2017)

    Article  Google Scholar 

  74. M. Laumanns, L. Thiele, K. Deb, E. Zitzler, Combining convergence and diversity in evolutionary multiobjective optimization. Evol. Comput. 10(3), 263–282 (2002)

    Article  Google Scholar 

  75. B. Li, J. Li, K. Tang, X. Yao, Many-objective evolutionary algorithms: a survey. ACM Comput. Surv. 48(1), 1–35 (2015)

    Article  MathSciNet  Google Scholar 

  76. H. Li, K. Deb, Q. Zhang, Variable-length Pareto optimization via decomposition-based evolutionary multiobjective algorithm. IEEE Trans. Evol. Comput. 23(6), 987–999 (2019)

    Article  Google Scholar 

  77. K. Li, K. Deb, T. Altinoz, X. Yao, Empirical investigations of reference point based methods when facing a massively large number of objectives: first results, in Evolutionary Multi-criterion Optimization (EMO) (Springer, 2017), pp. 390–405

    Google Scholar 

  78. J. Lin, H.-L. Liu, K.C. Tan, F. Gu, An effective knowledge transfer approach for multiobjective multitasking optimization. IEEE Trans. Cybern. 51(6), 3238–3248 (2021)

    Article  Google Scholar 

  79. Q. Lin, S. Liu, K.-C. Wong, M. Gong, C.A.C. Coello, A clustering-based evolutionary algorithm for many-objective optimization problems. IEEE Trans. Evol. Comput. 24(3), 391–405 (2019)

    Article  Google Scholar 

  80. Q. Liu, Y. **, M. Heiderich, T. Rodemann, An adaptive reference vector guided evolutionary algorithm using growing neural gas for many-objective optimization of irregular problems. IEEE Transactions on Cybernetics, 1–14 (2020)

    Google Scholar 

  81. Y. Liu, H. Ishibuchi, N. Masuyama, Y. Nojima, Adapting reference vectors and scalarizing functions by growing neural gas to handle irregular Pareto fronts. IEEE Trans. Evol. Comput. 24(3), 439–453 (2020)

    Google Scholar 

  82. J. Lu, B. Li, Y. **, An evolution strategy assisted by an ensemble of local Gaussian process models, in Genetic and Evolutionary Computation Conference (GECCO) (ACM Press, 2013), pp. 447–454

    Google Scholar 

  83. K. Miettinen, Nonlinear Multiobjective Optimization (Kluwer Academic Publishers, 1999)

    Google Scholar 

  84. K. Miettinen, P. Eskelinen, F. Ruiz, M. Luque, NAUTILUS method: An interactive technique in multiobjective optimization based on the nadir point. Eur. J. Oper. Res. 206(2), 426–434 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  85. K. Miettinen, J. Hakanen, D. Podkopaev, Interactive nonlinear multiobjective optimization methods, in Multiple Criteria Decision Analysis: State of the Art Surveys, 2nd edn. ed. by S. Greco, M. Ehrgott, J. Figueira (Springer, 2016), pp. 931–980

    Google Scholar 

  86. K. Miettinen, M. Mäkelä, Interactive bundle-based method for nondifferentiable multiobjective optimization: NIMBUS. Optimization 34, 231–246 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  87. K. Miettinen, M. Mäkelä, T. Männikkö, Optimal control of continuous casting by nondifferentiable multiobjective optimization. Comput. Optim. Appl. 11, 177–194 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  88. K. Miettinen, M.M. Mäkelä, Synchronous approach in interactive multiobjective optimization. Eur. J. Oper. Res. 170, 909–922 (2006)

    Article  MATH  Google Scholar 

  89. K. Miettinen, F. Ruiz, NAUTILUS framework: towards trade-off-free interaction in multiobjective optimization. J. Bus. Econ. 86(1–2), 5–21 (2016)

    Google Scholar 

  90. K. Miettinen, F. Ruiz, A. Wierzbicki, Introduction to multiobjective optimization: interactive approaches, in Multiobjective Optimization: Interactive and Evolutionary Approaches, ed. by J. Branke, K. Deb, K. Miettinen, R. Slowinski (Springer, 2008), pp. 27–57

    Google Scholar 

  91. E.A. Moallemi, F. Zare, P.M. Reed, S. Elsawah, M.J. Ryan, B.A. Bryan, Structuring and evaluating decision support processes to enhance the robustness of complex human-natural systems. Environ. Model. & Softw. 123, 104551 (2020)

    Article  Google Scholar 

  92. G. Misitano, B.S. Saini, B. Afsar, B. Shavazipour, K. Miettinen, DESDEO: the modular and open source framework for interactive multiobjective optimization, IEEE Access, 9, 148277–148295 (2021)

    Google Scholar 

  93. M.N. Omidvar, X. Li, Y. Mei, X. Yao, Cooperative co-evolution with differential grou** for large scale optimization. IEEE Trans. Evol. Comput. 18(3), 378–393 (2013)

    Article  Google Scholar 

  94. L. Pan, C. He, Y. Tian, H. Wang, X. Zhang, Y. **, A classification based surrogate-assisted evolutionary algorithm for expensive many-objective optimization. IEEE Trans. Evol. Comput. 23(1), 74–88 (2019)

    Article  Google Scholar 

  95. X. Peng, Y. **, H. Wang, Multi-modal optimization enhanced cooperative coevolution for large-scale optimization. IEEE Trans. Cybern. 49(9), 3507–3520 (2019)

    Article  Google Scholar 

  96. R. Purshouse, K. Deb, M. Mansor, S. Mostaghim, R. Wang, A review of hybrid evolutionary multiple criteria decision making methods, in Congress on Evolutionary Computation (CEC) (IEEE Press, 2014), pp. 1147–1154

    Google Scholar 

  97. R.C. Purshouse, P.J. Fleming, Conflict, harmony, and independence: relationships in evolutionary multi-criterion optimisation, in Evolutionary Multi-criterion Optimization (EMO) (Springer, 2003), pp. 16–30

    Google Scholar 

  98. M.S. Reed, A. Graves, N. Dandy, H. Posthumus, K. Hubacek, J. Morris, C. Prell, C.H. Quinn, L.C. Stringer, Who’s in and why? a typology of stakeholder analysis methods for natural resource management. J. Environ. Manag. 90(5), 1933–1949 (2009)

    Article  Google Scholar 

  99. A. Ruiz, F. Ruiz, K. Miettinen, L. Delgado-Antequera, V. Ojalehto, NAUTILUS Navigator: Free search interactive multiobjective optimization without trading-off. J. Global Optim. 74(2), 213–231 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  100. A.B. Ruiz, K. Sindhya, K. Miettinen, F. Ruiz, M. Luque, E-NAUTILUS: A decision support system for complex multiobjective optimization problems based on the NAUTILUS method. Eur. J. Oper. Res. 246, 218–231 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  101. M.L. Ryerkerk, R.C. Averill, K. Deb, E.D. Goodman, Meaningful representation and recombination of variable length genomes, in Genetic and Evolutionary Computation Conference (GECCO) (ACM Press, 2012), pp. 1471–1472

    Google Scholar 

  102. M.L. Ryerkerk, R.C. Averill, K. Deb, E.D. Goodman, Solving metameric variable-length optimization problems using genetic algorithms. Genet. Program Evolvable Mach. 18, 247–277 (2017)

    Article  Google Scholar 

  103. B. Saini, J. Hakanen, K. Miettinen, A new paradigm in interactive evolutionary multiobjective optimization, in Parallel Problem Solving from Nature (PPSN) (Springer, 2020), pp. 243–256

    Google Scholar 

  104. D.K. Saxena, J.A. Duro, A. Tiwari, K. Deb, Q. Zhang, Objective reduction in many-objective optimization: linear and nonlinear algorithms. IEEE Trans. Evol. Comput. 17(1), 77–99 (2013)

    Article  Google Scholar 

  105. B. Shahriari, K. Swersky, Z. Wang, R.P. Adams, N. de Freitas, Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104(1), 148–175 (2016)

    Article  Google Scholar 

  106. K. Sindhya, V. Ojalehto, J. Savolainen, H. Niemisto, J. Hakanen, K. Miettinen, Coupling dynamic simulation and interactive multiobjective optimization for complex problems: an APROS-NIMBUS case study. Expert Syst. Appl. 41(5), 2546–2558 (2014)

    Article  Google Scholar 

  107. H.K. Singh, A. Isaacs, T. Ray, A Pareto corner search evolutionary algorithm and dimensionality reduction in many-objective optimization problems. IEEE Trans. Evol. Comput. 15(4), 539–556 (2011)

    Article  Google Scholar 

  108. A. Sinha, P. Malo, K. Deb, A review on bilevel optimization: From classical to evolutionary approaches and applications. IEEE Trans. Evol. Comput. 22(2), 276–295 (2018)

    Article  Google Scholar 

  109. R. Srivastava, K. Deb, R. Tulsyan, An evolutionary algorithm based approach to design optimization using evidence theory. J. Mech. Des. 135(8), 081003 (2013)

    Article  Google Scholar 

  110. I. Steponavice, S. Ruuska, K. Miettinen, A solution process for simulation-based multiobjective design optimization with an application in paper industry. Comput. Aided Des. 47, 45–58 (2014)

    Article  Google Scholar 

  111. R.E. Steuer, Multiple Criteria Optimization: Theory, Computation and Application (Wiley, 1986)

    Google Scholar 

  112. C. Sun, Y. **, Y. Tan, Semi-supervised learning assisted particle swarm optimization of computationally expensive problems, in Genetic and Evolutionary Computation Conference (GECCO) (ACM Press, 2019), pp. 45–52

    Google Scholar 

  113. C. Sun, Y. **, J. Zeng, Y. Yu, A two-layer surrogate-assisted particle swarm optimization algorithm. Soft. Comput. 19(6), 1461–1475 (2015)

    Article  Google Scholar 

  114. X. Sun, D. Gong, Y. **, S. Chen, A new surrogate-assisted interactive genetic algorithm with weighted semi-supervised learning. IEEE Trans. Cybern. 43(2), 685–698 (2013)

    Article  Google Scholar 

  115. Y. Sun, G.G. Yen, Y. Zhang, Improved regularity model-based EDA for many-objective optimization. IEEE Trans. Evol. Comput. 22(5), 662–678 (2018)

    Article  Google Scholar 

  116. M. Tabatabaei, M. Hartikainen, K. Sindhya, J. Hakanen, K. Miettinen, An interactive surrogate-based method for computationally expensive multiobjective optimization. J. Oper. Res. Soc. 70(6), 898–914 (2019)

    Article  Google Scholar 

  117. J. Tian, Y. Tan, J. Zeng, C. Sun, Y. **, Multi-objective infill criterion driven Gaussian process assisted particle swarm optimization of high-dimensional expensive problems. IEEE Trans. Evol. Comput. 23(3), 459–472 (2019)

    Article  Google Scholar 

  118. Y. Tian, C. Lu, X. Zhang, K.C. Tan, Y. **, Solving large-scale multiobjective optimization problems with sparse optimal solutions via unsupervised neural networks. IEEE Trans. Cybern. (2021). To appear. https://doi.org/10.1109/TCYB.2020.2979930

  119. Y. Tian, S. Peng, T. Rodemann, X. Zhang, Y. **, Automated selection of evolutionary multi-objective optimization algorithms, in IEEE Symposium Series on Computational Intelligence (SSCI) (IEEE Press, 2019), pp. 3225–3232

    Google Scholar 

  120. B. Trindade, P. Reed, G. Characklis, Deeply uncertain pathways: integrated multi-city regional water supply infrastructure investment and portfolio management. Adv. Water Resour. 134, 103442 (2019)

    Article  Google Scholar 

  121. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, P.-A. Manzagol, Stacked denoising autoencoders: learning useful representationsina deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010)

    MathSciNet  MATH  Google Scholar 

  122. C. von Lücken, B. Barán, C. Brizuela, A survey on multi-objective evolutionary algorithms for many-objective problems. Comput. Optim. Appl. 58(3), 707–756 (2014)

    MathSciNet  MATH  Google Scholar 

  123. H. Wang, Y. **, J. Doherty, Committee-based active learning for surrogate-assisted particle swarm optimization of expensive problems. IEEE Trans. Cybern. 47(9), 2664–2677 (2017)

    Article  Google Scholar 

  124. H. Wang, Y. **, C. Sun, J. Doherty, Offline data-driven evolutionary optimization using selective surrogate ensembles. IEEE Trans. Evol. Comput. 23(2), 203–216 (2019)

    Article  Google Scholar 

  125. H. Wang, Y. **, C. Yang, L. Jiao, Transfer stacking from low- to high-fidelity: a surrogate-assisted bi-fidelity evolutionary algorithm. Appl. Soft Comput. 92, 106276 (2020)

    Article  Google Scholar 

  126. H. Wang, M. Olhofer, Y. **, A mini-review on preference modeling and articulation in multi-objective optimization: current status and challenges. Complex & Intell. Syst. 3(4), 233–245 (2017)

    Article  Google Scholar 

  127. H. Wang, X. Yao, Objective reduction based on nonlinear correlation information entropy. Soft. Comput. 20(6), 2393–2407 (2016)

    Article  Google Scholar 

  128. X. Wang, Y. **, S. Schmitt, M. Olhofer, Transfer learning for Gaussian process assisted evolutionary bi-objective optimization for objectives with different evaluation times, in Genetic and Evolutionary Computation Conference (GECCO) (ACM Press, 2020), pp. 587–594

    Google Scholar 

  129. T.B. Wild, P.M. Reed, D.P. Loucks, M. Mallen-Cooper, E.D. Jensen, Balancing hydropower development and ecological impacts in the Mekong: tradeoffs for Sambor Mega Dam. J. Water Resour. Plan. Manag. 145(2), 05018019 (2019)

    Article  Google Scholar 

  130. M.J. Woodruff, P.M. Reed, T.W. Simpson, Many objective visual analytics: rethinking the design of complex engineered systems. Struct. Multidiscip. Optim. 48(1), 201–219 (2013)

    Article  Google Scholar 

  131. C. Yang, J. Ding, Y. **, T. Chai, Off-line data-driven multi-objective optimization: knowledge transfer between surrogates and generation of final solutions. IEEE Trans. Evol. Comput. 24(3), 409–423 (2020)

    Google Scholar 

  132. Q. Yang, Y. Zhang, W. Dai, S. Pan, Transfer Learning (Cambridge University Press, Cambridge, 2020)

    Google Scholar 

  133. S. Yang, X. Yao, Evolutionary Computation for Dynamic Optimization Problems (Springer, 2013)

    Google Scholar 

  134. G. Yu, Y. **, M. Olhofer, A multi-objective evolutionary algorithm for finding knee regions using two localized dominance relationships. IEEE Trans. Evol. Comput. 25(1), 145–158 (2021)

    Article  Google Scholar 

  135. Y. Yuan, Y.S. Ong, A. Gupta, H. Xu, Objective reduction in many-objective optimization: evolutionary multiobjective approaches and comprehensive analysis. IEEE Trans. Evol. Comput. 22(2), 189–210 (2018)

    Article  Google Scholar 

  136. Q. Zhang, H. Li, MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)

    Article  Google Scholar 

  137. Q. Zhang, A. Zhou, Y. **, RM-MEDA: a regularity model-based multiobjective estimation of distribution algorithm. IEEE Trans. Evol. Comput. 12(1), 41–63 (2008)

    Article  Google Scholar 

  138. X. Zhang, Y. Tian, R. Cheng, Y. **, A decision variable clustering-based evolutionary algorithm for large-scale many-objective optimization. IEEE Trans. Evol. Comput. 22(1), 97–112 (2018)

    Article  Google Scholar 

  139. L. Zhou, L. Feng, K.C. Tan, J. Zhong, Z. Zhu, K. Liu, C. Chen, Toward adaptive knowledge transfer in multifactorial evolutionary computation. IEEE Trans. Cybern. (2021). To appear. https://doi.org/10.1109/TCYB.2020.2974100

  140. Y. Zhou-Kangas, K. Miettinen, Decision making in multiobjective optimization problems under uncertainty: balancing between robustness and quality. OR Spectrum 41(2), 391–413 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  141. Y. Zhou-Kangas, K. Miettinen, K. Sindhya, Solving multiobjective optimization problems with decision uncertainty: an interactive approach. J. Bus. Econ. 89(1), 25–51 (2019)

    Google Scholar 

  142. X. Zhu, A.B. Goldberg, Introduction to Semi-Supervised Learning. (Morgan & Claypool, 2009)

    Google Scholar 

Download references

Acknowledgements

Research reported in this chapter is related to the thematic research areas:

– Computational Optimization and Innovation (COIN) Laboratory at Michigan State University (+https://www.coin-lab.org+),

– Intelligent Systems, Decision and Control at The University of Sheffield,

– Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO, jyu.fi/demo) at the University of Jyvaskyla,

– Nature Inspired Computing and Engineering (NICE) Group at the University of Surrey, and

– The Decision Analytics for Complex Systems Group at Cornell University.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA

    Kalyanmoy Deb

  2. Department of Automatic Control and Systems Engineering, The University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Peter Fleming

  3. Department of Computer Science, University of Surrey, Guildford, Surrey, GU2 7XH, UK

    Yaochu **

  4. Faculty of Technology, Bielefeld University, 33619, Bielefeld, Germany

    Yaochu **

  5. University of Jyvaskyla, Faculty of Information Technology, P.O. Box 35 (Agora), FI-40014, University of Jyvaskyla, Finland

    Kaisa Miettinen

  6. Department of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA

    Patrick M. Reed

Authors
  1. Kalyanmoy Deb
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peter Fleming
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yaochu **
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kaisa Miettinen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Patrick M. Reed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter Fleming .

Editor information

Editors and Affiliations

  1. Inria Saclay-Île-de-France and CMAP, École Polytechnique, Palaiseau, France

    Dimo Brockhoff

  2. Leiden Institute of Advanced Computer Science, Leiden University, Leiden, The Netherlands

    Michael Emmerich

  3. Informatik und Ingenieurwissenschaften, TH Köln, Gummersbach, Germany

    Boris Naujoks

  4. Automatic Control and Systems Engineering, University of Sheffield, Sheffield, UK

    Robin Purshouse

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Deb, K., Fleming, P., **, Y., Miettinen, K., Reed, P.M. (2023). Key Issues in Real-World Applications of Many-Objective Optimisation and Decision Analysis. In: Brockhoff, D., Emmerich, M., Naujoks, B., Purshouse, R. (eds) Many-Criteria Optimization and Decision Analysis. Natural Computing Series. Springer, Cham. https://doi.org/10.1007/978-3-031-25263-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-25263-1_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25262-4

  • Online ISBN: 978-3-031-25263-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation