Abstract
A multi-objective optimization (MUOP) method that supports agile and flexible decision making to be able to handle complex and diverse decision environments has been in high demand. This study proposes a general idea for solving many-objective optimization (MAOP) problems by using the MOON2 or MOON2R method. These MUOP methods rely on prior articulation in trade-off analysis among conflicting objectives. Despite requiring only simple and relative responses, the decision maker’s trade-off analysis becomes rather difficult in the case of MAOP problems, in which the number of objective functions to be considered is larger than in MUOP. To overcome this difficulty, we present a stepwise procedure that is extensively used in the analytic hierarchy process. After that, the effectiveness of the proposed method is verified by applying it to an actual problem. Finally, a general discussion is presented to outline the direction of future work in this area.
This paper was presented at ICOTA 2013 held in Taipei., Taiwan.
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Shimizu, Y. (2015). An Extension of the MOON2/MOON2R Approach to Many-Objective Optimization Problems. In: Xu, H., Wang, S., Wu, SY. (eds) Optimization Methods, Theory and Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-47044-2_3
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