The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes
Abstract
Recently, a number of high performance many-objective evolutionary algorithms with systematically generated weight vectors have been proposed in the literature. Those algorithms often show surprisingly good performance on widely used DTLZ and WFG test problems. The performance of those algorithms has continued to be improved. The aim of this paper is to show our concern that such a performance improvement race may lead to the overspecialization of developed algorithms for the frequently used many-objective test problems. In this paper, we first explain the DTLZ and WFG test problems. Next, we explain many-objective evolutionary algorithms characterized by the use of systematically generated weight vectors. Then we discuss the relation between the features of the test problems and the search mechanisms of weight vector-based algorithms such as multiobjective evolutionary algorithm based on decomposition (MOEA/D), nondominated sorting genetic algorithm III (NSGA-III), MOEA/dominance and decomposition (MOEA/DD), and θ-dominance based evolutionary algorithm (θ-DEA). Through computational experiments, we demonstrate that a slight change in the problem formulations of DTLZ and WFG deteriorates the performance of those algorithms. After explaining the reason for the performance deterioration, we discuss the necessity of more general test problems and more flexible algorithms.
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Citations
Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
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Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach
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Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
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Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
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SPEA2: Improving the strength pareto evolutionary algorithm
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A niched Pareto genetic algorithm for multiobjective optimization
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A review of multiobjective test problems and a scalable test problem toolkit
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Cited by
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
Tian, Y., Cheng, R., Zhang, X., Cheng, F., & Jin, Y. (2018) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2017.2749619 · Cited by 699
Data-Driven Evolutionary Optimization: An Overview and Case Studies
Jin, Y., Wang, H., Chugh, T., Guo, D., & Miettinen, K. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2018.2869001 · Cited by 686
A Survey of Multiobjective Evolutionary Algorithms based on Decomposition
Trivedi, A., Srinivasan, D., Sanyal, K., & Ghosh, A. (2016) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2016.2608507 · Cited by 549
Tian, Y., Cheng, R., Zhang, X., Su, Y., & Jin, Y. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2018.2866854 · Cited by 384
Metadata
- Authors
- Ishibuchi, H., Setoguchi, Y., Masuda, H., & Nojima, Y.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2017-04
- Volume
- 21
- Issue
- 2
- Pages
- 169-190
- ISSN
- 1089-778X (ISSN-L)
- 1941-0026
- OpenAlex
- W2466060750
- Cited by
- 592
- References
- 40
- FWCI
- 53.59
- Type
- journal article
- Language
- en
- Open access
- closed
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- Ishibuchi, H., Setoguchi, Y., Masuda, H., & Nojima, Y. (2017). Performance of Decomposition-Based Many-Objective Algorithms Strongly Depends on Pareto Front Shapes. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/tevc.2016.2587749
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