An Extension of the MOON2/MOON2R Approach to Many-Objective Optimization Problems

Yoshiaki Shimizu ()
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Yoshiaki Shimizu: Toyohashi University of Technology, Department of Mechanical Engineering

Chapter Chapter 3 in Optimization Methods, Theory and Applications, 2015, pp 51-65 from Springer

Abstract: 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.

Keywords: Many-objective optimization; MOON2; MOON2R; Pairwise comparison; AHP; Neural network (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-662-47044-2_3

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DOI: 10.1007/978-3-662-47044-2_3

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