The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
An Indicator-Based Multiobjective Evolutionary Algorithm With Reference Point Adaptation for Better Versatility
Abstract
During the past two decades, a variety of multiobjective evolutionary algorithms (MOEAs) have been proposed in the literature. As pointed out in some recent studies, however, the performance of an MOEA can strongly depend on the Pareto front shape of the problem to be solved, whereas most existing MOEAs show poor versatility on problems with different shapes of Pareto fronts. To address this issue, we propose an MOEA based on an enhanced inverted generational distance indicator, in which an adaptation method is suggested to adjust a set of reference points based on the indicator contributions of candidate solutions in an external archive. Our experimental results demonstrate that the proposed algorithm is versatile for solving problems with various types of Pareto fronts, outperforming several state-of-the-art evolutionary algorithms for multiobjective and many-objective optimization.
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A fast and elitist multiobjective genetic algorithm: NSGA-II
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Multi-Objective Optimization Using Evolutionary Algorithms
Deb, K., & Kalyanmoy, D. (2001) · Andalas University Repository (Andalas University) · Cited by 14,918
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A review of multiobjective test problems and a scalable test problem toolkit
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Cited by
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
Metadata
- Authors
- Tian, Y., Cheng, R., Zhang, X., Cheng, F., & Jin, Y.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2018-08
- Volume
- 22
- Issue
- 4
- Pages
- 609-622
- ISSN
- 1089-778X (ISSN-L)
- 1941-0026
- OpenAlex
- W2751605210
- Cited by
- 699
- References
- 56
- FWCI
- 35.30
- Type
- journal article
- Language
- en
- Open access
- closed
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