Submitted to Global optimization: Theoretical foundations and applications, A. Abraham, A.-E. Hassanien, and P. Siarry (Eds.), “Studies in Computational Intelligence,” Springer-Verlag, 2008.
Experience has shown that a crafted combination of concepts of different metaheuristics can result in robust combinatorial optimization schemes and produce higher solution quality than the individual metaheuristics themselves, especially when solving difficult real-world combinatorial optimization problems. This chapter gives an overview of different ways to hybridize GRASP (Greedy Randomized Adaptive Search Procedures) to create new and more effective metaheuristics. Several types of hybridizations are considered, involving different constructive procedures, enhanced local search algorithms, and memory structures.
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