期刊
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 175, 期 2, 页码 931-947出版社
ELSEVIER
DOI: 10.1016/j.ejor.2005.06.019
关键词
multiple objective programming; classification; interactive methods; test problems; guaranteeing pareto optimality
In multiobjective optimization methods, the multiple conflicting objectives are typically converted into a single objective optimization problem with the help of scalarizing functions and such functions may be constructed in many ways. We compare both theoretically and numerically the performance of three classification-based scalarizing functions and pay attention to how well they obey the classification information. In particular, we devote special interest to the differences the scalarizing functions have in the computational cost of guaranteeing Pareto optimality. It turns out that scalarizing functions with or without so-called augmentation terms have significant differences in this respect. We also collect a set of mostly nonlinear benchmark test problems that we use in the numerical comparisons. (c) 2005 Elsevier B.V. All rights reserved.
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