Metaheuristics are smart problem solvers devoted to tackling particularly large optimization problems. During the last 20 years, they have largely been used to solve different problems from the academic as well as from the real-world. However, most of them have originally been designed for operating over real domain variables, being necessary to tailor its internal core, for instance, to be effective in a binary space of solutions. Various works have demonstrated that this internal modification, known as binarization, is not a simple task, since the several existing binarization ways may lead to very different results. This of course forces the user to implement and analyze a large list of binarization schemas for reaching good results. In this paper, we explore two efficient clustering methods, namely KMeans and DBscan to alter a metaheuristic in order to improve it, and thus do not require on the knowledge of an expert user for identifying which binarization strategy works better during the run. Both techniques have widely been applied to solve clustering problems, allowing us to exploit useful information gathered during the search to efficiently control and improve the binarization process. We integrate those techniques to a recent metaheuristic called Crow Search, and we conduct experiments where KMeans and DBscan are contrasted to 32 different binarization methods. The results show that the proposed approaches outperform most of the binarization strategies for a large list of well-known optimization instances.
Metaheuristics are intelligent problem-solvers that have been very efficient in solving huge optimization problems for more than two decades. However, the main drawback of these solvers is the need for problem-dependent and complex parameter setting in order to reach good results. This paper presents a new cuckoo search algorithm able to self-adapt its configuration, particularly its population and the abandon probability. The self-tuning process is governed by using machine learning, where cluster analysis is employed to autonomously and properly compute the number of agents needed at each step of the solving process. The goal is to efficiently explore the space of possible solutions while alleviating human effort in parameter configuration. We illustrate interesting experimental results on the well-known set covering problem, where the proposed approach is able to compete against various state-of-the-art algorithms, achieving better results in one single run versus 20 different configurations. In addition, the result obtained is compared with similar hybrid bio-inspired algorithms illustrating interesting results for this proposal.
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