Abstract
This paper proposes a novel multi-objective optimization algorithm, dual-stage nondominated sorting genetic algorithm-II (D-NSGA-II) for many-objective problems. Since the percentage of the nondominated solutions increases exponentially with the increasing number of objectives, just finding the nondominated solutions is not enough for solving many-objective problems. In other words, it is necessary to discriminate more meaningful ones from the other nondominated solutions by additionally incorporating user preference into the algorithms. The proposed D-NSGA-II can obtain not only user preference oriented, but also diverse nondominated solutions by introducing an additional stage of multi-objective optimization. The second stage employs the corresponding secondary objectives, global evaluation and crowding distance which were proposed in the previous research for representing the user’s preference to a solution and the crowdedness around a solution, respectively. To demonstrate the effectiveness of the proposed algorithm, some benchmark functions are tested and the outcomes of the proposed D-NSGA-II and the NSGA-II are empirically compared. Experimental results show that D-NSGA-II properly reflects the user’s preference in the optimization process as well as the performance in terms of the diversity and solution quality is competitive with the NSGA-II.
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Lee, KB. (2015). D-NSGA-II: Dual-Stage Nondominated Sorting Genetic Algorithm-II. In: Kim, JH., Yang, W., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 3. Advances in Intelligent Systems and Computing, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-319-16841-8_27
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DOI: https://doi.org/10.1007/978-3-319-16841-8_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16840-1
Online ISBN: 978-3-319-16841-8
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