Two metaheuristics for multiobjective stochastic combinatorial optimization

Authors:
Walter J. Gutjahr
Affiliations:
Dept. of Statistics and Decision Support Systems, University of Vienna
Venue:
SAGA'05 Proceedings of the Third international conference on StochasticAlgorithms: foundations and applications
Year:
2005

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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.