IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v210y2011i3p624-634.html
   My bibliography  Save this article

A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems

Author

Listed:
  • Zio, E.
  • Bazzo, R.

Abstract

In many multiobjective optimization problems, the Pareto Fronts and Sets contain a large number of solutions and this makes it difficult for the decision maker to identify the preferred ones. A possible way to alleviate this difficulty is to present to the decision maker a subset of a small number of solutions representatives of the Pareto Front characteristics. In this paper, a two-steps procedure is presented, aimed at identifying a limited number of representative solutions to be presented to the decision maker. Pareto Front solutions are first clustered into "families", which are then synthetically represented by a "head-of-the-family" solution. Level Diagrams are then used to represent, analyse and interpret the Pareto Front reduced to its head-of-the-family solutions. The procedure is applied to a reliability allocation case study of literature, in decision-making contexts both without or with explicit preferences by the decision maker on the objectives to be optimized.

Suggested Citation

  • Zio, E. & Bazzo, R., 2011. "A clustering procedure for reducing the number of representative solutions in the Pareto Front of multiobjective optimization problems," European Journal of Operational Research, Elsevier, vol. 210(3), pages 624-634, May.
  • Handle: RePEc:eee:ejores:v:210:y:2011:i:3:p:624-634
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377-2217(10)00662-4
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Katagiri, Hideki & Sakawa, Masatoshi & Kato, Kosuke & Nishizaki, Ichiro, 2008. "Interactive multiobjective fuzzy random linear programming: Maximization of possibility and probability," European Journal of Operational Research, Elsevier, vol. 188(2), pages 530-539, July.
    2. Roy, B. & Bouyssou, D., 1986. "Comparison of two decision-aid models applied to a nuclear power plant siting example," European Journal of Operational Research, Elsevier, vol. 25(2), pages 200-215, May.
    3. Gregory Levitin, 2005. "The Universal Generating Function in Reliability Analysis and Optimization," Springer Series in Reliability Engineering, Springer, number 978-1-84628-245-4, March.
    4. Yang, Jian-Bo, 2000. "Minimax reference point approach and its application for multiobjective optimisation," European Journal of Operational Research, Elsevier, vol. 126(3), pages 541-556, November.
    5. Molina, Julin & Santana, Luis V. & Hernandez-Daz, Alfredo G. & Coello Coello, Carlos A. & Caballero, Rafael, 2009. "g-dominance: Reference point based dominance for multiobjective metaheuristics," European Journal of Operational Research, Elsevier, vol. 197(2), pages 685-692, September.
    6. Zio, E. & Baraldi, P. & Pedroni, N., 2009. "Optimal power system generation scheduling by multi-objective genetic algorithms with preferences," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 432-444.
    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. Esmaili, Masoud & Shayanfar, Heidar Ali & Moslemi, Ramin, 2014. "Locating series FACTS devices for multi-objective congestion management improving voltage and transient stability," European Journal of Operational Research, Elsevier, vol. 236(2), pages 763-773.
    2. Compare, M. & Martini, F. & Zio, E., 2015. "Genetic algorithms for condition-based maintenance optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 244(2), pages 611-623.
    3. Khalili-Damghani, Kaveh & Amiri, Maghsoud, 2012. "Solving binary-state multi-objective reliability redundancy allocation series-parallel problem using efficient epsilon-constraint, multi-start partial bound enumeration algorithm, and DEA," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 35-44.
    4. Neuvonen, Lauri & Wildemeersch, Matthias & Vilkkumaa, Eeva, 2023. "Supporting strategy selection in multiobjective decision problems under uncertainty and hidden requirements," European Journal of Operational Research, Elsevier, vol. 307(1), pages 279-293.
    5. S. Ashbolt & S. Maheepala & B. Perera, 2014. "A Framework for Short-term Operational Planning for Water Grids," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2367-2380, June.
    6. Yu, Shiwei & Zheng, Shuhong & Gao, Shiwei & Yang, Juan, 2017. "A multi-objective decision model for investment in energy savings and emission reductions in coal mining," European Journal of Operational Research, Elsevier, vol. 260(1), pages 335-347.
    7. Su, Huai & Chi, Lixun & Zio, Enrico & Li, Zhenlin & Fan, Lin & Yang, Zhe & Liu, Zhe & Zhang, Jinjun, 2021. "An integrated, systematic data-driven supply-demand side management method for smart integrated energy systems," Energy, Elsevier, vol. 235(C).
    8. Perera, A.T.D. & Attalage, R.A. & Perera, K.K.C.K. & Dassanayake, V.P.C., 2013. "A hybrid tool to combine multi-objective optimization and multi-criterion decision making in designing standalone hybrid energy systems," Applied Energy, Elsevier, vol. 107(C), pages 412-425.
    9. Goerigk, Marc & Lendl, Stefan & Wulf, Lasse, 2022. "Recoverable robust representatives selection problems with discrete budgeted uncertainty," European Journal of Operational Research, Elsevier, vol. 303(2), pages 567-580.
    10. Petchrompo, Sanyapong & Wannakrairot, Anupong & Parlikad, Ajith Kumar, 2022. "Pruning Pareto optimal solutions for multi-objective portfolio asset management," European Journal of Operational Research, Elsevier, vol. 297(1), pages 203-220.
    11. Nicolas Dupin & Frank Nielsen & El-Ghazali Talbi, 2021. "Unified Polynomial Dynamic Programming Algorithms for P-Center Variants in a 2D Pareto Front," Mathematics, MDPI, vol. 9(4), pages 1-30, February.
    12. Dolatshahi-Zand, Ali & Khalili-Damghani, Kaveh, 2015. "Design of SCADA water resource management control center by a bi-objective redundancy allocation problem and particle swarm optimization," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 11-21.
    13. Wang, Yihan & Chen, Chen & Tao, Yuan & Wen, Zongguo & Chen, Bin & Zhang, Hong, 2019. "A many-objective optimization of industrial environmental management using NSGA-III: A case of China’s iron and steel industry," Applied Energy, Elsevier, vol. 242(C), pages 46-56.
    14. Fernández, Arturo J., 2012. "Minimizing the area of a Pareto confidence region," European Journal of Operational Research, Elsevier, vol. 221(1), pages 205-212.
    15. Jorge Salas & Víctor Yepes, 2019. "VisualUVAM: A Decision Support System Addressing the Curse of Dimensionality for the Multi-Scale Assessment of Urban Vulnerability in Spain," Sustainability, MDPI, vol. 11(8), pages 1-17, April.

    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. Zio, E. & Bazzo, R., 2011. "Level Diagrams analysis of Pareto Front for multiobjective system redundancy allocation," Reliability Engineering and System Safety, Elsevier, vol. 96(5), pages 569-580.
    2. Jinyi Hu, 2023. "Linguistic Multiple-Attribute Decision Making Based on Regret Theory and Minimax-DEA," Mathematics, MDPI, vol. 11(20), pages 1-14, October.
    3. Zhao, Xian & He, Zongda & Wu, Yaguang & Qiu, Qingan, 2022. "Joint optimization of condition-based performance control and maintenance policies for mission-critical systems," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    4. Hausken, Kjell & Levitin, Gregory, 2009. "Minmax defense strategy for complex multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 577-587.
    5. Yeh, Wei-Chang & Bae, Changseok & Huang, Chia-Ling, 2015. "A new cut-based algorithm for the multi-state flow network reliability problem," Reliability Engineering and System Safety, Elsevier, vol. 136(C), pages 1-7.
    6. Hindolo George-Williams & Geng Feng & Frank PA Coolen & Michael Beer & Edoardo Patelli, 2019. "Extending the survival signature paradigm to complex systems with non-repairable dependent failures," Journal of Risk and Reliability, , vol. 233(4), pages 505-519, August.
    7. Nourelfath, Mustapha & Ait-Kadi, Daoud, 2007. "Optimization of series–parallel multi-state systems under maintenance policies," Reliability Engineering and System Safety, Elsevier, vol. 92(12), pages 1620-1626.
    8. Bigatti, A.M. & Pascual-Ortigosa, P. & Sáenz-de-Cabezón, E., 2021. "A C++ class for multi-state algebraic reliability computations," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    9. Jia, Heping & Ding, Yi & Peng, Rui & Liu, Hanlin & Song, Yonghua, 2020. "Reliability assessment and activation sequence optimization of non-repairable multi-state generation systems considering warm standby," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    10. Lu, Shaoqi & Shi, Daimin & Xiao, Hui, 2019. "Reliability of sliding window systems with two failure modes," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 366-376.
    11. Peng, Rui & Xiao, Hui & Liu, Hanlin, 2017. "Reliability of multi-state systems with a performance sharing group of limited size," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 164-170.
    12. Hokkanen, Joonas & Salminen, Pekka, 1997. "Choosing a solid waste management system using multicriteria decision analysis," European Journal of Operational Research, Elsevier, vol. 98(1), pages 19-36, April.
    13. Bo, Yimin & Bao, Minglei & Ding, Yi & Hu, Yishuang, 2024. "A DNN-based reliability evaluation method for multi-state series-parallel systems considering semi-Markov process," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    14. Yeh, Wei-Chang, 2017. "Evaluation of the one-to-all-target-subsets reliability of a novel deterioration-effect acyclic multi-state information network," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 132-137.
    15. Levitin, Gregory & Xing, Liudong & Dai, Yuanshun, 2013. "Cold-standby sequencing optimization considering mission cost," Reliability Engineering and System Safety, Elsevier, vol. 118(C), pages 28-34.
    16. Rong Tang & Ke Li & Wei Ding & Yuntao Wang & Huicheng Zhou & Guangtao Fu, 2020. "Reference Point Based Multi-Objective Optimization of Reservoir Operation: a Comparison of Three Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1005-1020, February.
    17. Liu, Yu & Chen, Yiming & Jiang, Tao, 2020. "Dynamic selective maintenance optimization for multi-state systems over a finite horizon: A deep reinforcement learning approach," European Journal of Operational Research, Elsevier, vol. 283(1), pages 166-181.
    18. Niu, Yi-Feng & Gao, Zi-You & Lam, William H.K., 2017. "A new efficient algorithm for finding all d-minimal cuts in multi-state networks," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 151-163.
    19. Nahas, Nabil & Khatab, Abdelhakim & Ait-Kadi, Daoud & Nourelfath, Mustapha, 2008. "Extended great deluge algorithm for the imperfect preventive maintenance optimization of multi-state systems," Reliability Engineering and System Safety, Elsevier, vol. 93(11), pages 1658-1672.
    20. Yeh, Wei-Chang & Chu, Ta-Chung, 2018. "A novel multi-distribution multi-state flow network and its reliability optimization problem," Reliability Engineering and System Safety, Elsevier, vol. 176(C), pages 209-217.

    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:ejores:v:210:y:2011:i:3:p:624-634. 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/locate/eor .

    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.