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MCDA and Multiobjective Evolutionary Algorithms

In: Multiple Criteria Decision Analysis

Author

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  • Juergen Branke

    (University of Warwick)

Abstract

Evolutionary multiobjective optimization promises to efficiently generate a representative set of Pareto optimal solutions in a single optimization run. This allows the decision maker to select the most preferred solution from the generated set, rather than having to specify preferences a priori. In recent years, there has been a growing interest in combining the ideas of evolutionary multiobjective optimization and MCDA. MCDA can be used before optimization, to specify partial user preferences, after optimization, to help select the most preferred solution from the set generated by the evolutionary algorithm, or be tightly integrated with the evolutionary algorithm to guide the optimization towards the most preferred solution. This chapter surveys the state of the art of using preference information within evolutionary multiobjective optimization.

Suggested Citation

  • Juergen Branke, 2016. "MCDA and Multiobjective Evolutionary Algorithms," International Series in Operations Research & Management Science, in: Salvatore Greco & Matthias Ehrgott & José Rui Figueira (ed.), Multiple Criteria Decision Analysis, edition 2, chapter 0, pages 977-1008, Springer.
    • Handle: RePEc:spr:isochp:978-1-4939-3094-4_23
    DOI: 10.1007/978-1-4939-3094-4_23
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    Cited by:

    1. Fausto Balderas & Eduardo Fernandez & Claudia Gomez-Santillan & Nelson Rangel-Valdez & Laura Cruz, 2019. "An Interval-Based Approach for Evolutionary Multi-Objective Optimization of Project Portfolios," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1317-1358, July.
    2. Garcia Marrero, Luis Enrique & Arzola Ruíz, José, 2021. "Web-based tool for the decision making in photovoltaic/wind farms planning with multiple objectives," Renewable Energy, Elsevier, vol. 179(C), pages 2224-2234.
    3. E. Filatovas & O. Kurasova & J. L. Redondo & J. Fernández, 2020. "A reference point-based evolutionary algorithm for approximating regions of interest in multiobjective problems," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 402-423, July.

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