The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Constraint Handling in Multiobjective Evolutionary Optimization
Abstract
This paper proposes a constraint handling technique for multiobjective evolutionary algorithms based on an adaptive penalty function and a distance measure. These two functions vary dependent upon the objective function value and the sum of constraint violations of an individual. Through this design, the objective space is modified to account for the performance and constraint violation of each individual. The modified objective functions are used in the nondominance sorting to facilitate the search of optimal solutions not only in the feasible space but also in the infeasible regions. The search in the infeasible space is designed to exploit those individuals with better objective values and lower constraint violations. The number of feasible individuals in the population is used to guide the search process either toward finding more feasible solutions or favor in search for optimal solutions. The proposed method is simple to implement and does not need any parameter tuning. The constraint handling technique is tested on several constrained multiobjective optimization problems and has shown superior results compared to some chosen state-of-the-art designs.
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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
Multi-Objective Optimization Using Evolutionary Algorithms
Deb, K., & Kalyanmoy, D. (2001) · Andalas University Repository (Andalas University) · Cited by 14,918
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
SPEA2: Improving the strength pareto evolutionary algorithm
Zitzler, E., Laumanns, M., & Thiele, L. (2001) · doi:10.3929/ethz-a-004284029 · Cited by 5,008
Performance assessment of multiobjective optimizers: an analysis and review
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., & da Fonseca, V. G. (2003) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2003.810758 · Cited by 4,330
An efficient constraint handling method for genetic algorithms
Deb, K. (2000) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/s0045-7825(99)00389-8 · Cited by 3,892
Multiobjective Decision Making: Theory and Methodology
Chankong, V., & Haimes, Y. Y. (1983) · Medical Entomology and Zoology · Cited by 2,371
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Knowles, J. D., & Corne, D. W. (2000) · Evolutionary Computation · doi:10.1162/106365600568167 · Cited by 2,286
GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION
Fonseca, C. M., & Fleming, P. J. (1993) · Cited by 2,056
Stochastic ranking for constrained evolutionary optimization
Runarsson, T. P., & Yao, X. (2000) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.873238 · Cited by 1,807
Cited by
Push and pull search for solving constrained multi-objective optimization problems
Fan, Z., Li, W., Cai, X., Li, H., Wei, C., Zhang, Q., Deb, K., & Goodman, E. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2018.08.017 · Cited by 594
Liu, Z.-Z., & Wang, Y. (2019) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2019.2894743 · Cited by 486
Metadata
- Authors
- Woldesenbet, Y. G., Yen, G. G., & Tessema, B. G.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2009-06
- Volume
- 13
- Issue
- 3
- Pages
- 514-525
- ISSN
- 1089-778X (ISSN-L)
- 1941-0026
- OpenAlex
- W2143585163
- Cited by
- 420
- References
- 33
- FWCI
- 11.91
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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