A review of Pareto pruning methods for multi-objective optimization
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Authors
Petchrompo, S
Coit, DW
Brintrup, A
Wannakrairot, A
Parlikad, AK
Publication Date
2022-05Journal Title
Computers and Industrial Engineering
ISSN
0360-8352
Publisher
Elsevier BV
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Petchrompo, S., Coit, D., Brintrup, A., Wannakrairot, A., & Parlikad, A. (2022). A review of Pareto pruning methods for multi-objective optimization. Computers and Industrial Engineering https://doi.org/10.1016/j.cie.2022.108022
Abstract
Previous researchers have made impressive strides in developing algorithms and solution methodologies to address multi-objective optimization (MOO) problems in industrial engineering and associated fields. One traditional approach is to determine a Pareto optimal set that represents the trade-off between objectives. However, this approach could result in an extremely large set of solutions, making it difficult for the decision maker to identify the most promising solutions from the Pareto front. To deal with this issue, later contributors proposed alternative approaches that can autonomously draw up a shortlist of Pareto optimal solutions so that the results are more comprehensible to the decision maker. These alternative approaches are referred to as the pruning method in this review. The selection of the representative solutions in the pruning method is based on a predefined instruction, and its resolution process is mostly independent of the decision maker. To systematize studies on this aspect, we first provide the definitions of the pruning method and related terms; then, we establish a new classification of MOO methods to distinguish the pruning method from the a priori, a posteriori, and interactive methods. To facilitate readers in identifying a method that suits their interests, we further classify the pruning method by the instruction on how the representative solutions are selected, namely into the preference-based, diversity-based, efficiency-based, and problem specific methods. Ultimately, the comparative analysis of the pruning method and other MOO approaches allows us to provide insights into the current trends in the field and offer recommendations on potential research directions.
Sponsorship
Engineering and Physical Sciences Research Council (EP/R004935/1)
EPSRC (via Lancaster University) (Unknown)
Embargo Lift Date
2023-08-19
Identifiers
External DOI: https://doi.org/10.1016/j.cie.2022.108022
This record's URL: https://www.repository.cam.ac.uk/handle/1810/334282
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Licence URL: https://creativecommons.org/licenses/by-nc-nd/4.0/
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