Integrated multiobjective optimization and a priori preferences using genetic algorithms

  • Authors:

  • Javier Sanchis;Miguel A. Martínez;Xavier Blasco

  • Affiliations:

  • Grupo de Control Predictivo y Optimización Heurística (CPOH), Departamento de Ingeniería de Sistemas y Automática, Universidad Politécnica de Valencia, Camino de Vera s/n, ...;Grupo de Control Predictivo y Optimización Heurística (CPOH), Departamento de Ingeniería de Sistemas y Automática, Universidad Politécnica de Valencia, Camino de Vera s/n, ...;Grupo de Control Predictivo y Optimización Heurística (CPOH), Departamento de Ingeniería de Sistemas y Automática, Universidad Politécnica de Valencia, Camino de Vera s/n, ...

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2008

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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, e.g. 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 information 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 performed. Problems found due to the multimodality nature of a generated single cost index are managed with Genetic Algorithms (GAs). Three examples are given to illustrate the effectiveness of the method.