Metaheuristics for dynamic combinatorial optimization problems

Metaheuristics for dynamic combinatorial optimization problems

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Article ID: iaor20135026
Volume: 24
Issue: 4
Start Page Number: 451
End Page Number: 480
Publication Date: Oct 2013
Journal: IMA Journal of Management Mathematics
Authors: , ,
Keywords: metaheuristics
Abstract:

Many real‐world optimization problems are combinatorial optimization problems subject to dynamic environments. In such dynamic combinatorial optimization problems (DCOPs), the objective, decision variables and/or constraints may change over time, and so solving DCOPs is a challenging task. Metaheuristics are a good choice of tools to tackle DCOPs because many metaheuristics are inspired by natural or biological evolution processes, which are always subject to changing environments. In recent years, DCOPs have attracted a growing interest from the metaheuristics community. This paper is a tutorial on metaheuristics for DCOPs. We cover the definition of DCOPs, typical benchmark problems and their characteristics, methodologies and performance measures, real‐world case study and key challenges in the area. Some future research directions are also pointed out in this paper.

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