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Multi-objective Optimization

In: Search Methodologies

Author

Listed:
  • Kalyanmoy Deb

    (Michigan State University
    Michigan State University
    Michigan State University)

  • Kalyanmoy Deb

    (Michigan State University
    Michigan State University
    Michigan State University)

Abstract

Multi-objective optimization is an integral part of optimization activities and has a tremendous practical importance, since almost all real-world optimization problems are ideally suited to be modeled using multiple conflicting objectives. The classical means of solving such problems were primarily focused on scalarizing multiple objectives into a single objective, whereas the evolutionary means have been to solve a multi-objective optimization problem as it is. In this chapter, we discuss the fundamental principles of multi-objective optimization, the differences between multi-objective optimization and single-objective optimization, and describe a few well-known classical and evolutionary algorithms for multi-objective optimization. Two application case studies reveal the importance of multi-objective optimization in practice. A number of research challenges are then highlighted. The chapter concludes by suggesting a few tricks of the trade and mentioning some key resources to the field of multi-objective optimization.

Suggested Citation

  • Kalyanmoy Deb & Kalyanmoy Deb, 2014. "Multi-objective Optimization," Springer Books, in: Edmund K. Burke & Graham Kendall (ed.), Search Methodologies, edition 2, chapter 0, pages 403-449, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-6940-7_15
    DOI: 10.1007/978-1-4614-6940-7_15
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    Cited by:

    1. Waranyoo Thippo & Chorkaew Jaturanonda & Sorawit Yaovasuwanchai & Charoenchai Khompatraporn & Teeradej Wuttipornpun & Kulwara Meksawan, 2024. "Multi-Objective Job Rotation in Rice Seed Harvesting With Equitable Injury Risk and Cost Allocation," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 15(1), pages 1-28, January.
    2. Lai, Wenhao & Zheng, Xiaoliang & Song, Qi & Hu, Feng & Tao, Qiong & Chen, Hualiang, 2022. "Multi-objective membrane search algorithm: A new solution for economic emission dispatch," Applied Energy, Elsevier, vol. 326(C).
    3. Macias, A. & Kandidayeni, M. & Boulon, L. & Trovão, J.P., 2021. "Fuel cell-supercapacitor topologies benchmark for a three-wheel electric vehicle powertrain," Energy, Elsevier, vol. 224(C).
    4. Zhang, Xinyue & Guo, Xiaopeng & Zhang, Xingping, 2023. "Bidding modes for renewable energy considering electricity-carbon integrated market mechanism based on multi-agent hybrid game," Energy, Elsevier, vol. 263(PA).
    5. Biswas, Dhrupad & Ghosh, Susenjit & Sengupta, Somnath & Mukhopadhyay, Siddhartha, 2022. "Energy Management of a Parallel Hybrid Electric Vehicle using Model Predictive Static Programming," Energy, Elsevier, vol. 250(C).
    6. Michela Dalle Mura & Francesco Pistolesi & Gino Dini & Beatrice Lazzerini, 2021. "End-of-life product disassembly with priority-based extraction of dangerous parts," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 837-854, March.
    7. Sadjady Naeeni, Hannan & Sabbaghi, Navid, 2022. "Sustainable supply chain network design: A case of the glass manufacturer in Asia," International Journal of Production Economics, Elsevier, vol. 248(C).
    8. 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).
    9. Nondy, J. & Gogoi, T.K., 2022. "Tri-objective optimization of two recuperative gas turbine-based CCHP systems and 4E analyses at optimal conditions," Applied Energy, Elsevier, vol. 323(C).
    10. Sadeghi, Mohammad & Yaghoubi, Saeed, 2024. "Optimization models for cloud seeding network design and operations," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1146-1167.
    11. Esteves, Elisa M.M. & Brigagão, George V. & Morgado, Cláudia R.V., 2021. "Multi-objective optimization of integrated crop-livestock system for biofuels production: A life-cycle approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    12. Haywood, Adam B. & Lunday, Brian J. & Robbins, Matthew J. & Pachter, Meir N., 2022. "The weighted intruder path covering problem," European Journal of Operational Research, Elsevier, vol. 297(1), pages 347-358.
    13. Nondy, J. & Gogoi, T.K., 2021. "Performance comparison of multi-objective evolutionary algorithms for exergetic and exergoenvironomic optimization of a benchmark combined heat and power system," Energy, Elsevier, vol. 233(C).
    14. Velásquez, Laura & Posada, Alejandro & Chica, Edwin, 2023. "Surrogate modeling method for multi-objective optimization of the inlet channel and the basin of a gravitational water vortex hydraulic turbine," Applied Energy, Elsevier, vol. 330(PB).
    15. Wegel, Sebastian & Ivanov, Anton & Lenz, Ralf & Volling, Thomas, 2024. "Scheduling of parallel continuous annealing lines with alternative processing modes to optimize efficiency under tardiness constraints," European Journal of Operational Research, Elsevier, vol. 316(1), pages 282-294.
    16. Smedberg, Henrik & Bandaru, Sunith, 2023. "Interactive knowledge discovery and knowledge visualization for decision support in multi-objective optimization," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1311-1329.
    17. van Schilt, Isabelle M. & van Kalker, Jonna & Lefter, Iulia & Kwakkel, Jan H. & Verbraeck, Alexander, 2024. "Buffer scheduling for improving on-time performance and connectivity with a multi-objective simulation–optimization model: A proof of concept for the airline industry," Journal of Air Transport Management, Elsevier, vol. 115(C).
    18. Liu, Ming & Lin, Tao & Chu, Feng & Ding, Yueyu & Zheng, Feifeng & Chu, Chengbin, 2023. "Bi-objective optimization for supply chain ripple effect management under disruption risks with supplier actions," International Journal of Production Economics, Elsevier, vol. 265(C).

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