The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Survey of quality measures for multi-objective optimization: Construction of complementary set of multi-objective quality measures
Abstract
Abstract In recent years interest in multiobjective optimization has flourished. Many Quality Measures (QM) have been developed to allow comparison of results gained by many methods. Unfortunately significant amount of various QMs along with the lack of imposed taxonomy have caused vagueness in the naming conventions. Hence a cohesive taxonomy is proposed that allows for classification of both existing and future QMs. This paper additionally provides thorough description of recently used QMs while attempting to unify the nomenclature. Advantages and disadvantages are shown along with the various features of the measures in given problem – as an example Multi–Skill Resource Constrained Project Scheduling Problem is given. Finally, a complementary set of QMs is proposed that can create a meaningful comparison of obtained multiobjective solutions to a multiobjective problem. Supplementary measures are proposed for specialized applications and open issues in the field are identified.
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“However, many real-life problems can be characterized as multi-objective problems involving multiple conflicting objectives that should be considered simultaneously [51, 52].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [52]
Citations
Cites
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Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
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Bosman, P. A. N., & Thierens, D. (2003) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2003.810761 · Cited by 1,154
Evolutionary many-objective optimization: A short review
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A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization
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Research works printed but not linked: 1
- Wu, 2001, Metrics for quality assessment of a multiobjective design optimization solution set, J. Mech. Des., 123, 18, 10.1115/1.1329875 · DOI · Paper page
Cited by
Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68
“However, many real-life problems can be characterized as multi-objective problems involving multiple conflicting objectives that should be considered simultaneously [51, 52].”
Background
Metadata
- Authors
- Laszczyk, M., & Myszkowski, P. B.
- Affiliations
- Wrocław University of Science and Technology (PL)
- Corresponding author
- Myszkowski, Paweł B.
- ORCID
- Myszkowski, Paweł B.
- Venue
- Swarm and Evolutionary Computation
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 48
- Pages
- 109-133
- ISSN
- 2210-6502 (ISSN-L)
- OpenAlex
- W2932401226
- Cited by
- 79
- References
- 61
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 7 · 2023: 9 · 2022: 15 · 2021: 17 · 2020: 10 · 2019: 4
- FWCI
- 7.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Swarm and Evolutionary Computation (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
- Social Sciences › Decision Sciences › Management Science and Operations Research › Resource-Constrained Project Scheduling
- Social Sciences › Decision Sciences › Management Science and Operations Research › Multi-Criteria Decision Making
- Keywords
- multi-objective optimization, quality metrics, comparison of solutions
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- CoChat. (2026). Citation check: Survey of quality measures for multi-objective optimization: Construction of complementary set of multi-objective quality measures. https://app.cochat.ai/papers/10.1016/j.swevo.2019.04.001
- The paper
- Laszczyk, M., & Myszkowski, P. B. (2019). Survey of quality measures for multi-objective optimization: Construction of complementary set of multi-objective quality measures. Swarm and Evolutionary Computation. https://doi.org/10.1016/j.swevo.2019.04.001
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
