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Two Metaheuristics for Multiobjective Stochastic Combinatorial Optimization

  • Walter J. Gutjahr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3777)

Abstract

Two general-purpose metaheuristic algorithms for solving multiobjective stochastic combinatorial optimization problems are introduced: SP-ACO (based on the Ant Colony Optimization paradigm) which combines the previously developed algorithms S-ACO and P-ACO, and SPSA, which extends Pareto Simulated Annealing to the stochastic case. Both approaches are tested on random instances of a TSP with time windows and stochastic service times.

Keywords

Ant colony optimization combinatorial optimization multiobjective decision analysis simulated annealing stochastic optimization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Walter J. Gutjahr
    • 1
  1. 1.Dept. of Statistics and Decision Support SystemsUniversity of Vienna 

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