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.
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Gutjahr, W.J. (2005). Two Metaheuristics for Multiobjective Stochastic Combinatorial Optimization. In: Lupanov, O.B., Kasim-Zade, O.M., Chaskin, A.V., Steinhöfel, K. (eds) Stochastic Algorithms: Foundations and Applications. SAGA 2005. Lecture Notes in Computer Science, vol 3777. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11571155_12
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DOI: https://doi.org/10.1007/11571155_12
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