Journal
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
Volume 22, Issue 1, Pages 47-60Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2016.2634625
Keywords
Genetic algorithm; interval; many-objective optimization; set-based evolution; uncertainty
Funding
- National Natural Science Foundation of China [61375067, 61403155, 61673404]
- National Basic Research Program of China (973 Program) [2014CB0463062]
- Jiangsu Overseas Research and Training Program for University Prominent Young and Middle-Aged Teachers and Presidents
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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.
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