Abstract
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.
Highlights
We propose to use an unsupervised machine learning technique that let us learning in the search space of the metaheuristic, exploiting the characteristic of their attributes that let us use to enhance the metaheuristic parameters in an online way: spatial clustering based on noise application density (DBSCAN) is one of those techniques that gathers those characteristics
To compare a hybrid algorithm that resembles our proposal, we have considered making a comparison of results with hybrid algorithms that work with bio-inspired metaheuristics, improved by machine learning (ML), and they solve the set covering problem
The second studied approach was the integration between the crow search algorithm and the Kmean method (CSAKmean)
Summary
Recent studies about bio-inspired procedures to solve complex optimization problems have demonstrated that finding good results and the best performance are laborious tasks, so it is necessary to apply an off-line parameter adjustment on metaheuristics [1,2,3,4,5]. This adjustment is considered an optimization problem itself, and several studies are proposing some solutions to solve that, but it always depends on his static therms [6]. We consider using a machine learning (ML) technique that lets us analyze the population and determine the values of the number of solutions and the abandon probability
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