Two Metaheuristics for Multiobjective Stochastic Combinatorial Optimization | Zendy

Walter J. Gutjahr | Zendy

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Two Metaheuristics for Multiobjective Stochastic Combinatorial Optimization

Author(s) -

Walter J. Gutjahr

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-29498-8

DOI - 10.1007/11571155_12

Subject(s) - metaheuristic , ant colony optimization algorithms , computer science , mathematical optimization , simulated annealing , parallel metaheuristic , combinatorial optimization , stochastic optimization , extremal optimization , multi objective optimization , pareto principle , algorithm , meta optimization , mathematics

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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