The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
Abstract
Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EAs are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the strength Pareto EA (SPEA), that combines several features of previous multiobjective EAs in a unique manner. It is characterized by (a) storing nondominated solutions externally in a second, continuously updated population, (b) evaluating an individual's fitness dependent on the number of external nondominated points that dominate it, (c) preserving population diversity using the Pareto dominance relationship, and (d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EAs on the 0/1 knapsack problem.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Research works printed but not linked: 47
- 10.1162/evco.1993.1.2.127 · DOI
- smith, 1992, population diversity in an immune system model: implications for genetic search, Foundations of Genetic Algorithms 2 (FOGA-92)
- oei, 1991, Tournament selection niching and the preservation of diversity
- 10.1162/evco.1996.4.4.361 · DOI · Paper page
- martello, 1990, Knapsack Problems Algorithms and Computer Implementations
- 10.1109/ICEC.1996.542368 · DOI
- 10.1007/BFb0029771 · DOI
- holland, 1975, Adaption in Natural and Artificial Systems
- chambers, 1983, Graphical Methods for Data Analysis
- deb, 1989, an investigation of niche and species formation in genetic function optimization, Proc 3rd Int Conf Genetic Algorithms, 42
- kursawe, 1992, evolution strategies for vector optimization, Proc 10th Int Conf Multiple Criteria Decision Making, 187
- 10.1162/evco.1993.1.3.191 · DOI
- 10.1007/BF01759923 · DOI
- 10.1109/ICEC.1996.542345 · DOI
- goldberg, 1989, Genetic Algorithms in Search Optimization and Machine Learning
- fonseca, 1993, genetic algorithms for multiobjective optimization: formulation, discussion and generalization, Proc 5th Int Conf Genetic Algorithms, 416
- greenwood, 1996, fitness functions for multiple objective optimization problems: combining preferences with pareto rankings, Foundations of Genetic Algorithms 4 (FOGA-96), 437
- mahfoud, 1995, Niching methods for genetic algorithms
- goldberg, 1987, genetic algorithms with sharing for multimodal function optimization, Genetic Algorithms and Their Applications Proceedings of the Second International Conference on Genetic Algorithms, 41
- horn, 1993, Multiobjective optimization using the niched Pareto genetic algorithm
- todd, 1997, a multiple criteria genetic algorithm for containership loading, Proc 7th Int Conf Genetic Algorithms, 674
- michalewicz, 1994, genetic algorithms for the 0/1 knapsack problem, Methodologies for Intelligent Systems (ISMIS 94), 134, 10.1007/3-540-58495-1_14 · DOI
- 10.1887/0750308958/b386c85 · DOI
- 10.1145/326619.326694 · DOI · Paper page
- 10.1162/evco.1994.2.3.221 · DOI · Paper page
- 10.1007/BFb0056872 · DOI · Paper page
- 10.1109/ICSMC.1995.537834 · DOI
- 10.1007/978-1-4615-3612-3 · DOI
- steuer, 1986, Multiple Criteria Optimization Theory Computation Application
- valenzuela-rendo´n, 1997, a nongenerational genetic algorithm for multiobjective optimization, Proc 7th Int Conf Genetic Algorithms, 658
- 10.1162/evco.1995.3.1.1 · DOI · Paper page
- fourman, 1985, compaction of symbolic layout using genetic algorithms, Proc Int Conf Genetic Algorithms and Their Applications, 141
- 10.1109/HSC.1997.584597 · DOI
- cunha, 1997, use of genetic algorithms in multicriteria optimization to solve industrial problems, Proc 7th Int Conf Genetic Algorithms, 682
- zitzler, 1999, Comparison of multiobjective evolutionary algorithms Empirical results
- harik, 1995, finding multimodal solutions using restricted tournament selection, Proc 6h Int Conf Genetic Algorithms, 24
- de jong, 1975, An analysis of the bevavior of a class of genetic adaptive systems
- 10.1023/A:1008899229802 · DOI
- loughlin, 1997, the neighborhood constraint-method: a genetic algorithm-based multiobjective optimization technique, Proc 7th Int Conf Genetic Algorithms, 666
- 10.1016/0305-0548(80)90014-3 · DOI
- blickle, 1996, Theory of evolutionary algorithms and application to system-synthesis
- paredis, 1995, the symbiotic evolution of solutions and their representations, Proc 7th Int Conf Genetic Algorithms, 359
- ryan, 1995, niche and species formation in genetic algorithms, Practical Handbook of Genetic Algorithms, 1, 57
- 10.1109/ICEC.1994.350037 · DOI
- schaffer, 1984, Multiple objective optimization with vector evaluated genetic algorithms
- 10.1080/03052158508902489 · DOI
- 10.1109/ICEC.1996.542653 · DOI
Cited by
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
Zhang, Q., & Li, H. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2007.892759 · Cited by 10,069
Platemo: a matlab platform for evolutionary multi-objective optimization [educational forum]
Tian, Y., Cheng, R., Zhang, X., & Jin, Y. (2017) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2017.2742868 · Cited by 2,777
Pareto Multi Objective Optimization
Ngatchou, P., Zarei, A., & El-Sharkawi, A. (2006) · Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems · doi:10.1109/isap.2005.1599245 · Cited by 664
Deb, K., Mohan, M., & Mishra, S. (2005) · Evolutionary Computation · doi:10.1162/106365605774666895 · Cited by 637
A practical guide to multi-objective reinforcement learning and planning
Hayes, C. F., Rădulescu, R., Bargiacchi, E., Källström, J., Macfarlane, M., Reymond, M., Verstraeten, T., Zintgraf, L. M., Dazeley, R., Heintz, F., et al. (2022) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-022-09552-y · Cited by 400
Techniques for highly multiobjective optimisation: some nondominated points are better than others
Corne, D. W., & Knowles, J. D. (2007) · Proceedings of the 9th annual conference on Genetic and evolutionary computation · doi:10.1145/1276958.1277115 · Cited by 273
Scalarized multi-objective reinforcement learning: Novel design techniques
Van Moffaert, K., Drugan, M. M., & Nowe, A. (2013) · 2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL) · doi:10.1109/adprl.2013.6615007 · Cited by 253
Quantum Optimization and Quantum Learning: A Survey
Li, Y., Tian, M., Liu, G., Peng, C., & Jiao, L. (2020) · IEEE Access · doi:10.1109/access.2020.2970105 · Cited by 177
MOGOA algorithm for constrained and unconstrained multi-objective optimization problems
Tharwat, A., Houssein, E. H., Ahmed, M. M., Hassanien, A. E., & Gabel, T. (2018) · Applied Intelligence · doi:10.1007/s10489-017-1074-1 · Cited by 114
Laszczyk, M., & Myszkowski, P. B. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.04.001 · Cited by 79
MOEA based memetic algorithms for multi-objective satellite range scheduling problem
Du, Y., Xing, L., Zhang, J., Chen, Y., & He, Y. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.100576 · Cited by 69
Directed Search: A New Operator in NSGA-II for Task Scheduling in IoT Based on Cloud-Fog Computing
Mousavi, S., Mood, S. E., Souri, A., & Javidi, M. M. (2023) · IEEE Transactions on Cloud Computing · doi:10.1109/tcc.2022.3188926 · Cited by 51
Liang, J., Li, Z.-W., Yue, C.-T., Hu, Z., Cheng, H., Liu, Z.-X., & Guo, W.-F. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac254 · Cited by 23
Concept of Interactive Machine Learning in Urban Design Problems
Chirkin, A. M., & König, R. (2016) · Proceedings of the SEACHI 2016 on Smart Cities for Better Living with HCI and UX · doi:10.1145/2898365.2899795 · Cited by 18
Medium Formulation and Development
Link, H., & Weuster-Botz, D. (2011) · Comprehensive Biotechnology · doi:10.1016/b978-0-08-088504-9.00092-1 · Cited by 11
Multi-Layer LEO Constellation Optimization Based on D-NSDE Algorithm
Wang, S., Zhuang, X., Wu, C., Fan, G., Li, M., Xu, T., & Zhao, X. (2025) · Remote Sensing · doi:10.3390/rs17060994 · Cited by 10
Hinds, C. A. (2016) · California Polytechnic State University (MSc Thesis) · doi:10.15368/theses.2014.189 · Cited by 1
Metadata
- Authors
- Zitzler, E., & Thiele, L.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 1999
- Volume
- 3
- Issue
- 4
- Pages
- 257-271
- ISSN
- 1089-778X (ISSN-L)
- OpenAlex
- W2106334424
- Cited by
- 8,798
- References
- 1
- FWCI
- 75.73
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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.
