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Three Approaches for Solving the Stochastic Multiobjective Programming Problem

  • Norio Baba
  • Akira Morimoto
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 379)

Abstract

In this paper, we consider the multiobjective optimization problem in which each objective function is disturbed by noise. Three approaches using learning automata, random optimization method, and stochastic approximation method are proposed to solve this problem. It is shown that these three approaches are able to find appropriate solutions of this problem. Several computer simulation results also confirm our theoretical study.

Keywords

Stochastic Approximation Aspiration Level Multiobjective Optimization Problem Multiobjective Program Learning Automaton 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Norio Baba
    • 1
  • Akira Morimoto
    • 2
  1. 1.Osaka Educational UniversityIkeca City, 563Japan
  2. 2.Kyoto UniversityKyoto City, 606Japan

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