TY - JOUR
T1 - A KNN quantum cuckoo search algorithm applied to the multidimensional knapsack problem
AU - GARCIA CONEJEROS, JOSE ANTONIO
AU - Maureira, Carlos
N1 - Funding Information:
Jos? Garc?a was supported by the Grant CONICYT/FONDECYT/INICIACION, Chile /11180056.
Funding Information:
José García was supported by the Grant CONICYT/FONDECYT/INICIACION, Chile / 11180056 .
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/4
Y1 - 2021/4
N2 - Optimization algorithms and particularly metaheuristics are constantly improved with the goal of reducing execution times, increasing the quality of solutions, and addressing larger target instances. Hybridizing techniques are one of these methods particularly interesting for the broad scope of problems to which they can be adapted. In this work, we assessed a hybrid algorithm that uses the k-nearest neighbor technique to improve the results of a quantum cuckoo search algorithm for resource allocation. The k-nearest neighbor technique is used to direct the movement of solutions. Numerical experiments were performed to obtain insights from the contribution of the k-nearest neighbor technique in the final result of solutions. The well-known multidimensional knapsack problem was addressed in order to validate our procedure; a comparison is made with state-of-the-art algorithms. Our results show that our hybrid algorithm consistently produces better results in most of the analyzed instances.
AB - Optimization algorithms and particularly metaheuristics are constantly improved with the goal of reducing execution times, increasing the quality of solutions, and addressing larger target instances. Hybridizing techniques are one of these methods particularly interesting for the broad scope of problems to which they can be adapted. In this work, we assessed a hybrid algorithm that uses the k-nearest neighbor technique to improve the results of a quantum cuckoo search algorithm for resource allocation. The k-nearest neighbor technique is used to direct the movement of solutions. Numerical experiments were performed to obtain insights from the contribution of the k-nearest neighbor technique in the final result of solutions. The well-known multidimensional knapsack problem was addressed in order to validate our procedure; a comparison is made with state-of-the-art algorithms. Our results show that our hybrid algorithm consistently produces better results in most of the analyzed instances.
KW - Combinatorial optimization
KW - KNN
KW - Machine learning
KW - Metaheuristics
KW - Multidimensional knapsack problem
UR - http://www.scopus.com/inward/record.url?scp=85098987078&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2020.107077
DO - 10.1016/j.asoc.2020.107077
M3 - Article
AN - SCOPUS:85098987078
VL - 102
JO - Applied Soft Computing Journal
JF - Applied Soft Computing Journal
SN - 1568-4946
M1 - 107077
ER -