A taxonomy of low-level hybridization in metaheuristics algorithms

Abstract

In the last two decades, a lot of metaheuristics approaches have been discovered to tackle large-scale of combinatorial optimization problems. Among the approaches, one of the most effective is so-called metaheuristics hybridization that tries to combine different strengths of different algorithms. In hybridization techniques, implementing low-level hybridization is considered as the most complicated due to the internal structure modification of the hybrid algorithms. In addition, different components of the hybrid algorithms are strongly inter-dependent and they must fit will together in solving a particular problem. Therefore, determining appropriate components to be retained and dropped or replaced in each of metaheuristic algorithm is a very difficult task. Responding to the complexity, this paper presents a new taxonomy for low-level hybridization. Then, a review of several implementations for low-level hybridization in metaheuristics is given with regards to the taxonomy. The outcome of study is useful in providing guidance for effective implementation of low-level hybridization.