Two metaheuristics for multiobjective stochastic combinatorial optimization

被引：0
|
作者：
Gutjahr, WJ [1 ]
机构：
[1] Univ Vienna, Dept Stat & Decis Support Syst, A-1010 Vienna, Austria
来源：
STOCHASTIC ALGORITHMS: FOUNDATIONS AND APPLICATIONS, PROCEEDINGS | 2005年 / 3777卷
关键词：
ant colony optimization; combinatorial optimization; multiobjective decision analysis; simulated annealing; stochastic optimization;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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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页码：116 / 125
页数：10
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