IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v122y2024ics0305048323000890.html
   My bibliography  Save this article

Explainable interactive evolutionary multiobjective optimization

Author

Listed:
  • Corrente, Salvatore
  • Greco, Salvatore
  • Matarazzo, Benedetto
  • Słowiński, Roman

Abstract

We present a new approach to Interactive Evolutionary Multiobjective Optimization (IEMO) guided by a preference elicitation procedure inspired by artificial intelligence and designed in line with decision psychology. For a conflicting nature of objectives, a solution optimizing all objectives simultaneously does not exist and thus the best compromise solution is searched for. In the interactive search, preference elicitation phases alternate with optimization phases. In the IEMO procedure proposed in this paper, during the preference elicitation phase, the Decision Maker (DM) gets a sample of solutions from the current population and is asked to indicate relatively good solutions in this sample. Using the Dominance-based Rough Set Approach (DRSA), this information is summarized by “if ..., then ... ” decision rules which represent DM’s preferences. They are used in the next optimization phases of the IEMO to influence the crossover so as to converge towards the part of the Pareto front containing the best compromise solution. Besides guiding the search process, the decision rules can be read as arguments explaining the DM’s preferences. In this way, the proposed method implements the postulate of transparency and explainability expected from interactive procedures. The DM has a chance to understand how their answers given in the preference elicitation phase are translated into guidelines for the algorithm in the optimization phase. This is a distinctive aspect of what we call eXplainable Interactive Multiobjective Evolutionary optimization Approach (XIMEA). The presented XIMEA-DRSA method implements this approach. From the viewpoint of behavioral psychology, the decision rules support the DM to construct and learn their preferences in the course of evolutionary optimization. To check the efficiency of XIMEA-DRSA, we performed several experiments on continuous as well as combinatorial test problems, assuming the existence of an artificial DM that iteratively provides its preferences according to a known value function. The results prove that XIMEA-DRSA is converging to the most interesting part of the Pareto front, similar to an evolutionary algorithm that optimizes the value function of the artificial DM and, therefore, the latter is used as a benchmark.

Suggested Citation

  • Corrente, Salvatore & Greco, Salvatore & Matarazzo, Benedetto & Słowiński, Roman, 2024. "Explainable interactive evolutionary multiobjective optimization," Omega, Elsevier, vol. 122(C).
    • Handle: RePEc:eee:jomega:v:122:y:2024:i:c:s0305048323000890
    DOI: 10.1016/j.omega.2023.102925
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048323000890
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.omega.2023.102925?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pedro Bordalo & Nicola Gennaioli & Andrei Shleifer, 2012. "Salience Theory of Choice Under Risk," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(3), pages 1243-1285.
    2. Jyrki Wallenius, 1975. "Comparative Evaluation of Some Interactive Approaches to Multicriterion Optimization," Management Science, INFORMS, vol. 21(12), pages 1387-1396, August.
    3. Wu, Xueqi & Che, Ada, 2020. "Energy-efficient no-wait permutation flow shop scheduling by adaptive multi-objective variable neighborhood search," Omega, Elsevier, vol. 94(C).
    4. Selcen (Pamuk) Phelps & Murat Köksalan, 2003. "An Interactive Evolutionary Metaheuristic for Multiobjective Combinatorial Optimization," Management Science, INFORMS, vol. 49(12), pages 1726-1738, December.
    5. Luque, Mariano & Miettinen, Kaisa & Eskelinen, Petri & Ruiz, Francisco, 2009. "Incorporating preference information in interactive reference point methods for multiobjective optimization," Omega, Elsevier, vol. 37(2), pages 450-462, April.
    6. P. L. Yu, 1973. "A Class of Solutions for Group Decision Problems," Management Science, INFORMS, vol. 19(8), pages 936-946, April.
    7. James G. March, 1978. "Bounded Rationality, Ambiguity, and the Engineering of Choice," Bell Journal of Economics, The RAND Corporation, vol. 9(2), pages 587-608, Autumn.
    8. Branke, Juergen & Corrente, Salvatore & Greco, Salvatore & Słowiński, Roman & Zielniewicz, Piotr, 2016. "Using Choquet integral as preference model in interactive evolutionary multiobjective optimization," European Journal of Operational Research, Elsevier, vol. 250(3), pages 884-901.
    9. Greco, Salvatore & Matarazzo, Benedetto & Slowinski, Roman, 2001. "Rough sets theory for multicriteria decision analysis," European Journal of Operational Research, Elsevier, vol. 129(1), pages 1-47, February.
    10. Goerigk, Marc & Hartisch, Michael, 2023. "A framework for inherently interpretable optimization models," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1312-1324.
    11. Saul Gass & Thomas Saaty, 1955. "The computational algorithm for the parametric objective function," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 2(1‐2), pages 39-45, March.
    12. Kadziński, Miłosz & Tervonen, Tommi & Tomczyk, Michał K. & Dekker, Rommert, 2017. "Evaluation of multi-objective optimization approaches for solving green supply chain design problems," Omega, Elsevier, vol. 68(C), pages 168-184.
    13. Mota, Caroline Maria de Miranda & Figueiredo, Ciro José Jardim de & Pereira, Débora Viana e Sousa, 2021. "Identifying areas vulnerable to homicide using multiple criteria analysis and spatial analysis," Omega, Elsevier, vol. 100(C).
    14. Barbati, Maria & Greco, Salvatore & Kadziński, Miłosz & Słowiński, Roman, 2018. "Optimization of multiple satisfaction levels in portfolio decision analysis," Omega, Elsevier, vol. 78(C), pages 192-204.
    15. Chica, Manuel & Bautista, Joaquín & Cordón, Óscar & Damas, Sergio, 2016. "A multiobjective model and evolutionary algorithms for robust time and space assembly line balancing under uncertain demand," Omega, Elsevier, vol. 58(C), pages 55-68.
    16. Dasdemir, Erdi & Testik, Murat Caner & Öztürk, Diclehan Tezcaner & Şakar, Ceren Tuncer & Güleryüz, Güldal & Testik, Özlem Müge, 2022. "A multi-objective open vehicle routing problem with overbooking: Exact and heuristic solution approaches for an employee transportation problem," Omega, Elsevier, vol. 108(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sariyer, Gorkem & Mangla, Sachin Kumar & Sozen, Mert Erkan & Li, Guo & Kazancoglu, Yigit, 2024. "Leveraging explainable artificial intelligence in understanding public transportation usage rates for sustainable development," Omega, Elsevier, vol. 127(C).
    2. Eyke Hüllermeier & Roman Słowiński, 2024. "Preference learning and multiple criteria decision aiding: differences, commonalities, and synergies—part II," 4OR, Springer, vol. 22(3), pages 313-349, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Barbati, Maria & Corrente, Salvatore & Greco, Salvatore, 2020. "A general space-time model for combinatorial optimization problems (and not only)," Omega, Elsevier, vol. 96(C).
    2. Barbati, Maria & Greco, Salvatore & Kadziński, Miłosz & Słowiński, Roman, 2018. "Optimization of multiple satisfaction levels in portfolio decision analysis," Omega, Elsevier, vol. 78(C), pages 192-204.
    3. Greco, Salvatore & Mousseau, Vincent & Slowinski, Roman, 2008. "Ordinal regression revisited: Multiple criteria ranking using a set of additive value functions," European Journal of Operational Research, Elsevier, vol. 191(2), pages 416-436, December.
    4. Kadziński, Miłosz & Ciomek, Krzysztof, 2021. "Active learning strategies for interactive elicitation of assignment examples for threshold-based multiple criteria sorting," European Journal of Operational Research, Elsevier, vol. 293(2), pages 658-680.
    5. Diaz-Balteiro, L & González-Pachón, J. & Romero, C., 2017. "Measuring systems sustainability with multi-criteria methods: A critical review," European Journal of Operational Research, Elsevier, vol. 258(2), pages 607-616.
    6. Salvatore Corrente & Salvatore Greco & Benedetto Matarazzo & Roman Słowiński, 2016. "Robust ordinal regression for decision under risk and uncertainty," Journal of Business Economics, Springer, vol. 86(1), pages 55-83, January.
    7. Mariano Luque & Ana Ruiz & Rubén Saborido & Óscar Marcenaro-Gutiérrez, 2015. "On the use of the $$L_{p}$$ L p distance in reference point-based approaches for multiobjective optimization," Annals of Operations Research, Springer, vol. 235(1), pages 559-579, December.
    8. De Bock, Koen W. & Coussement, Kristof & Caigny, Arno De & Słowiński, Roman & Baesens, Bart & Boute, Robert N. & Choi, Tsan-Ming & Delen, Dursun & Kraus, Mathias & Lessmann, Stefan & Maldonado, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.
    9. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    10. Mike G. Tsionas, 2021. "Multi-criteria optimization in regression," Annals of Operations Research, Springer, vol. 306(1), pages 7-25, November.
    11. Liu, Jiapeng & Kadziński, Miłosz & Liao, Xiuwu & Mao, Xiaoxin & Wang, Yao, 2020. "A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples," European Journal of Operational Research, Elsevier, vol. 286(3), pages 963-985.
    12. Sung-Shun Weng & Yang Liu & Juan Dai & Yen-Ching Chuang, 2020. "A Novel Improvement Strategy of Competency for Education for Sustainable Development (ESD) of University Teachers Based on Data Mining," Sustainability, MDPI, vol. 12(7), pages 1-18, March.
    13. Julio Cezar Soares Silva & Diogo Ferreira de Lima Silva & Luciano Ferreira & Adiel Teixeira de Almeida-Filho, 2022. "A dominance-based rough set approach applied to evaluate the credit risk of sovereign bonds," 4OR, Springer, vol. 20(1), pages 139-164, March.
    14. Miłosz Kadziński & Michał K. Tomczyk, 2017. "Interactive Evolutionary Multiple Objective Optimization for Group Decision Incorporating Value-based Preference Disaggregation Methods," Group Decision and Negotiation, Springer, vol. 26(4), pages 693-728, July.
    15. Tsionas, Mike G., 2018. "A Bayesian approach to find Pareto optima in multiobjective programming problems using Sequential Monte Carlo algorithms," Omega, Elsevier, vol. 77(C), pages 73-79.
    16. José Antón & Ana Tarquis & Juan Grau & Elena Sánchez & Antonio Saa & Mari-Cruz Díaz, 2014. "Application of Decision Theory methods for a soil classification in the Community of Madrid (Spain)," Annals of Operations Research, Springer, vol. 219(1), pages 203-229, August.
    17. Wang, Rui & Purshouse, Robin C. & Giagkiozis, Ioannis & Fleming, Peter J., 2015. "The iPICEA-g: a new hybrid evolutionary multi-criteria decision making approach using the brushing technique," European Journal of Operational Research, Elsevier, vol. 243(2), pages 442-453.
    18. Figueira, José Rui & Greco, Salvatore & Slowinski, Roman, 2009. "Building a set of additive value functions representing a reference preorder and intensities of preference: GRIP method," European Journal of Operational Research, Elsevier, vol. 195(2), pages 460-486, June.
    19. Sarnataro, Michele & Barbati, Maria & Greco, Salvatore, 2021. "A portfolio approach for the selection and the timing of urban planning projects," Socio-Economic Planning Sciences, Elsevier, vol. 75(C).
    20. Costa, Ana Sara & Figueira, José Rui & Borbinha, José, 2018. "A multiple criteria nominal classification method based on the concepts of similarity and dissimilarity," European Journal of Operational Research, Elsevier, vol. 271(1), pages 193-209.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jomega:v:122:y:2024:i:c:s0305048323000890. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.