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A niched Pareto genetic algorithm for multiobjective optimization
Abstract
Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The genetic algorithm (GA), however, is readily modified to deal with multiple objectives by incorporating the concept of Pareto domination in its selection operator, and applying a niching pressure to spread its population out along the Pareto optimal tradeoff surface. We introduce the Niched Pareto GA as an algorithm for finding the Pareto optimal set. We demonstrate its ability to find and maintain a diverse "Pareto optimal population" on two artificial problems and an open problem in hydrosystems.>
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Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Muiltiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
Srinivas, N., & Deb, K. (1994) · Evolutionary Computation · doi:10.1162/evco.1994.2.3.221 · Cited by 6,935
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Multi-objective optimization using genetic algorithms: A tutorial
Konak, A., Coit, D. W., & Smith, A. E. (2006) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2005.11.018 · Cited by 3,440
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Tian, Y., Cheng, R., Zhang, X., & Jin, Y. (2017) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2017.2742868 · Cited by 2,777
Metadata
- Authors
- Horn, J., Nafpliotis, N., & Goldberg, D. E.
- Venue
- Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence
- Publisher
- IEEE
- Published
- 2002-12-17
- Pages
- 82-87
- OpenAlex
- W1905847227
- Cited by
- 2,433
- References
- 0
- FWCI
- 79.57
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Horn, J., Nafpliotis, N., & Goldberg, D. E. (2002). A niched Pareto genetic algorithm for multiobjective optimization. Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence. https://doi.org/10.1109/icec.1994.350037
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