Abstract
Interval multi-objective optimization problems (MOPs) are popular and important in real-world applications. We present a novel interactive evolutionary algorithm (IEA) incorporating an optimization-cum-decision-making procedure to obtain the most preferred solution that fits a decision-maker (DM)’s preferences. Our method is applied to two interval MOPs and compared with PPIMOEA and the posteriori method, and the experimental results confirm the superiorities of our method.
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Sun, J., Gong, D., Sun, X. (2011). Optimizing Interval Multi-objective Problems Using IEAs with Preference Direction. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_52
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DOI: https://doi.org/10.1007/978-3-642-24958-7_52
Publisher Name: Springer, Berlin, Heidelberg
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