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Solving Rotated Multi-objective Optimization Problems Using Differential Evolution
Open accessAbstract
This paper demonstrates that the self-adaptive technique of Differential Evolution (DE) can be simply used for solving a multi-objective optimization problem where parameters are interdependent The real-coded crossover and mutation rates within the NSGA-II have been replaced with a simple Differential Evolution scheme, and results are reported on a rotated problem which has presented difficulties using existing Multi-objective Genetic Algorithms The Differential Evolution variant of the NSGA-II has demonstrated rotational invariance and superior performance over the NSGA-II on this problem.
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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
Cited by
Differential Evolution: A Survey of the State-of-the-Art
Das, S., & Suganthan, P. N. (2011) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2010.2059031 · Cited by 5,307
Differential evolution algorithm with ensemble of parameters and mutation strategies
Mallipeddi, R., Suganthan, P. N., Pan, Q. K., & Tasgetiren, M. F. (2011) · Applied Soft Computing · doi:10.1016/j.asoc.2010.04.024 · Cited by 1,362
Covariance Matrix Adaptation for Multi-objective Optimization
Igel, C., Hansen, N., & Roth, S. (2007) · Evolutionary Computation · doi:10.1162/evco.2007.15.1.1 · Cited by 788
Differential evolution with multi-population based ensemble of mutation strategies
Wu, G., Mallipeddi, R., Suganthan, P. N., Wang, R., & Chen, H. (2016) · Information Sciences · doi:10.1016/j.ins.2015.09.009 · Cited by 569
Differential Evolution With Ranking-Based Mutation Operators
Gong, W., & Cai, Z. (2013) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2013.2239988 · Cited by 401
Metadata
- Authors
- Iorio, A. W., & Li, X.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2004
- Pages
- 861-872
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2106307115
- Cited by
- 263
- References
- 1
- FWCI
- 3.65
- Type
- conference paper
- Language
- en
- Open access
- green
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- Iorio, A. W., & Li, X. (2004). Solving Rotated Multi-objective Optimization Problems Using Differential Evolution. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-30549-1_74
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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.
