English

103 Pages

Downloading requires you to have access to the YouScribe library

__
Learn all about the services we offer
__

Description

Evolutionary Multiobjective Optimization:Past, Present and FutureCarlos A. Coello CoelloCINVESTAV-IPNDepto. de Ingenier´ıa El´ectricaSecci´ on de Computaci´ onAv. Instituto Polit´ecnico Nacional No. 2508Col. San Pedro ZacatencoM´exico, D. F. 07300, MEXICOccoello@cs.cinvestav.mx1MotivationMost problems in nature have several (possibly conﬂicting)objectives to be satisﬁed. Many of these problems are frequentlytreated as single-objective optimization by transformingall but one objective into constraints.2What is a multiobjective optimization problem?The Multiobjective Optimization Problem (MOP) (alsocalled multicriteria optimization, multiperformance or vectoroptimization problem) can be deﬁned (in words) as the problem ofﬁnding (Osyczka, 1985):a vector of decision variables which satisﬁes constraints andoptimizes a vector function whose elements represent theobjective functions. These functions form a mathematicaldescription of performance criteria which are usually inconﬂict with each other. Hence, the term “optimize” meansﬁnding such a solution which would give the values of allthe objective functions acceptable to the decision maker.3A Formal DeﬁnitionThe general Multiobjective Optimization Problem (MOP) can beformally deﬁned as:T∗ ∗ ∗ ∗Find the vector~x = [x ,x ,...,x ] which will satisfy the m1 2 ninequality constraints:g (~x)≥ 0 i = 1, 2,...,m (1)ithe p equality constraintsh (~x) = 0 i = 1, 2,...,p (2)iand will optimize the ...

Subjects

Informations

Published by | Pheyn |

Reads | 19 |

Language | English |

Report a problem