HYBRID METAHEURISTICS FOR OPTIMIZATION
HYBRID METAHEURISTICS FOR OPTIMIZATION

Summary/Abstract: Metaheuristics algorithms give good approximate solutions forreal-world applications of large size. They also include a set of effective methods for difficult optimization problems. These methods are generally classified into two categories: methods for local search that are based on the intensification strategy and methods for global search that rely on diversification. In order to have relevant results, must try to achieve a balance between these two strategies. The hybridization methods can be used to find an improvement so the advantages and disadvantages of each method are compensated. If an appropriate hybridization technique is chosen, the resulting hybrid metaheuristic is an attempt to over perform both the original metaheuristics, by designing a new better optimization tool. This paper is a survey of some concepts and methods that are involved in the hybridization of metaheuristics and discuses some taxonomies proposed in the literature.

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