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Concepts, Theory, and Techniques PRINCIPLES OF MULTIOBJECTIVE OPTIMIZATION *
Author(s) -
Rosenthal Richard E.
Publication year - 1985
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1985.tb01479.x
Subject(s) - maximization , computer science , rubric , mathematical optimization , multi objective optimization , set (abstract data type) , simple (philosophy) , class (philosophy) , strengths and weaknesses , principal (computer security) , management science , artificial intelligence , mathematics , machine learning , economics , operating system , philosophy , arithmetic , epistemology , programming language
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.