A Set-Based Genetic Algorithm for Interval Many-Objective Optimization Problems

Interval many-objective optimization problems (IMaOPs), involving more than three objectives and at least one subjected to interval uncertainty, are ubiquitous in real-world applications. However, there have been very few effective methods for solving these problems. In this paper, we proposed a set...

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Veröffentlicht in:IEEE transactions on evolutionary computation Jg. 22; H. 1; S. 47 - 60
Hauptverfasser: Gong, Dunwei, Sun, Jing, Miao, Zhuang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.02.2018
Schlagworte:
Context
Convergence
Genetic algorithm
Genetic algorithms
interval
many-objective optimization
Noise measurement
Optimization
set-based evolution
Sorting
Uncertainty
ISSN:1089-778X, 1941-0026
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Zusammenfassung:Interval many-objective optimization problems (IMaOPs), involving more than three objectives and at least one subjected to interval uncertainty, are ubiquitous in real-world applications. However, there have been very few effective methods for solving these problems. In this paper, we proposed a set-based genetic algorithm to effectively solve them. The original optimization problem was first transformed into a deterministic bi-objective problem, where new objectives are hyper-volume and imprecision. A set-based Pareto dominance relation was then defined to modify the fast nondominated sorting approach in NSGA-II. Additionally, set-based evolutionary schemes were suggested. Finally, our method was empirically evaluated on 39 benchmark IMaOPs as well as a car cab design problem and compared with two typical methods. The numerical results demonstrated the superiority of our method and indicated that a tradeoff approximate front between convergence and uncertainty can be produced.
ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2016.2634625