A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique

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 present...

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Main Authors: Nicolás Caselli, Ricardo Soto, Broderick Crawford, Sergio Valdivia, Rodrigo Olivares
Format: Article
Language:English
Published: MDPI AG 2021-08-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/9/16/1840
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author Nicolás Caselli
Ricardo Soto
Broderick Crawford
Sergio Valdivia
Rodrigo Olivares
author_facet Nicolás Caselli
Ricardo Soto
Broderick Crawford
Sergio Valdivia
Rodrigo Olivares
author_sort Nicolás Caselli
collection DOAJ
description 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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spelling doaj.art-ce3c86fdfe7541a3b162af74f3bbf5ab2023-11-22T08:32:43ZengMDPI AGMathematics2227-73902021-08-01916184010.3390/math9161840A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning TechniqueNicolás Caselli0Ricardo Soto1Broderick Crawford2Sergio Valdivia3Rodrigo Olivares4Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, ChileDirección de Tecnologías de Información y Comunicación, Universidad de Valparaíso, Valparaíso 2361864, ChileEscuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362905, ChileMetaheuristics 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.https://www.mdpi.com/2227-7390/9/16/1840clustering techniquesmetaheuristicsmachine learningself-adaptiveparameter settingexploration
spellingShingle Nicolás Caselli
Ricardo Soto
Broderick Crawford
Sergio Valdivia
Rodrigo Olivares
A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique
Mathematics
clustering techniques
metaheuristics
machine learning
self-adaptive
parameter setting
exploration
title A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique
title_full A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique
title_fullStr A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique
title_full_unstemmed A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique
title_short A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique
title_sort self adaptive cuckoo search algorithm using a machine learning technique
topic clustering techniques
metaheuristics
machine learning
self-adaptive
parameter setting
exploration
url https://www.mdpi.com/2227-7390/9/16/1840
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