Concepts, Theory, and Techniques PRINCIPLES OF MULTIOBJECTIVE OPTIMIZATION*
梳理多目标优化的核心思想，评估每种方法的优缺点，重点分析效率与效用两个概念，并推荐三类有前景的技术：部分生成有效集、显式效用最大化和交互式隐式效用最大化。
ABSTRACT This paper attempts to isolate and analyze the principal ideas of multiobjective optimization. We do this without casting aspersions on single‐objective optimization or championing any one multiobjective technique. We examine each fundamental idea for strengths and weaknesses and subject two—efficiency and utility—to extended consideration. Some general recommendations are made in light of this analysis. Besides the simple advice to retain single‐objective optimization as a possible approach, we suggest that three broad classes of multiobjective techniques are very promising in terms of reliably, and believably, achieving a most preferred solution. These are: (1) partial generation of the efficient set , a rubric we use to unify a wide spectrum of both interactive and analytic methods; (2) explicit utility maximization , a much‐overlooked approach combining multiattribute decision theory and mathematical programming; and (3) interactive implicit utility maximization , the popular class of methods introduced by Geoffrion, Dyer, and Feinberg [24] and extended significantly by others.