Metaheuristics for Stochastic Problems
Lars Magnus Hvattum, Eystein Fredrik Esbensen · Wiley Encyclopedia of Operations Research and Management Science · 2011
Abstract A large number of publications have been written about metaheuristics and their use in solving deterministic optimization problems. As metaheuristic techniques become more widespread in the production of decision support systems, it is clear that they also need to cope with problems, based on real‐world applications, that contain noise and uncertainty. An increasing amount of work is being put into the study of metaheuristics for stochastic problems, and into how metaheuristics must be adapted to efficiently handle different sources of stochasticity. We give examples of how metaheuristics have been used to solve stochastic problems, thereby illustrating the additional considerations that are required in this context.