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Information Sciences 178 (2008) 931–951
ate/ins
Integrated multiobjective optimization and a priori
preferences using ic algorithms
Javier Sanchis *, Miguel A. Martı´nez, Xavier Blasco
Grupo de Control Predictivo y Optimizacio´n Heurı´stica (CPOH), Departamento de Ingenierı´a de Sistemas y Automa´tica,
Universidad ica de Valencia, Camino de Vera s/n, 46022 Valencia, Spain
Received 13 March 2007; received in revised form 19 September 2007; accepted 19 September 2007
Abstract
One of the tasks of decision-making support systems is to develop methods that help the designer select a solution
among a set of actions, . by constructing a function expressing his/her preferences over a set of potential solutions.
In this paper, a new method to solve multiobjective optimization (MOO) problems is developed in which the user’s infor-
mation about his/her preferences is taken into account within the search process. Preference functions are built that reflect
the decision-maker’s (DM) interests and use meaningful parameters for each objective. The preference functions convert
these objective preferences into numbers. Next, a single objective is automatically built and no weight selection is per-
formed. Problems found due to the multimodality nature of a generated single cost index are managed with ic Algo-
rithms (GAs). Three examples are given to illustrate the effectiveness of the method.
Ó 2007 Elsevier Inc. All rights reserved.
Keywords: Multiobjective optimization; Engineering design; Preference functions; Decision-making; ic algorithms
1. Introduction
System design is plex task when design parameters have to meet a number of often conflicting spec-
ifications or objectives. Hence, system design can be understood as the search for the promise among
all the required specifications. This challenging problem is called multiobjective optimization (MOO).
The solution to a MOO problem is not unique as the best solution for all