TY - GEN
T1 - Population Size Management in a Cuckoo Search Algorithm Solving Combinatorial Problems
AU - Chávez, Marcelo
AU - Crawford, Broderick
AU - Soto, Ricardo
AU - Palma, Wenceslao
AU - Becerra-Rozas, Marcelo
AU - Cisternas-Caneo, Felipe
AU - Astorga, Gino
AU - Misra, Sanjay
N1 - Funding Information:
Broderick Crawford and Wenceslao Palma are supported by Grant ANID/FONDECYT/REGULAR/1210810. Ricardo Soto is supported by grant CONICYT/FONDECYT/REGULAR/1190129. Marcelo Becerra-Rozas is supported by National Agency for Research and Development (ANID)/Scholarship Pro-gram/DOCTORADO NACIONAL/2021-21210740. Broderick Crawford, Ricardo Soto and Marcelo Becerra-Rozas are supported by Grant Nucleo de Investigacion en Data Analytics/VRIEA/PUCV/039.432/2020. Marcelo Becerra-Rozas are supported by Grant DI Investigaci?n Interdisciplinaria del Pregrado/VRIEA/PUCV/039.421/2021.
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - As is well known in the scientific community, optimization problems are becoming increasingly common, complex, and difficult to solve. The use of metaheuristics to solve these problems is gaining momentum thanks to their great adaptability. Because of this, there is a need to generate robust metaheuristics with a good balance of exploration and exploitation for different optimization problems. Our proposal seeks to improve the exploration and exploitation balance by incorporating a dynamic variation of the population. For this purpose, we implement the Cuckoo Search metaheuristic in its two versions, with and without dynamic population, to solve 3 classical optimization problems. Preliminary results are very good in terms of performance but indicate that it is not enough to vary the population dynamically, but it is necessary to add additional perturbation operators to force changes in the metaheuristic behavior.
AB - As is well known in the scientific community, optimization problems are becoming increasingly common, complex, and difficult to solve. The use of metaheuristics to solve these problems is gaining momentum thanks to their great adaptability. Because of this, there is a need to generate robust metaheuristics with a good balance of exploration and exploitation for different optimization problems. Our proposal seeks to improve the exploration and exploitation balance by incorporating a dynamic variation of the population. For this purpose, we implement the Cuckoo Search metaheuristic in its two versions, with and without dynamic population, to solve 3 classical optimization problems. Preliminary results are very good in terms of performance but indicate that it is not enough to vary the population dynamically, but it is necessary to add additional perturbation operators to force changes in the metaheuristic behavior.
KW - Combinatorial problems
KW - Cuckoo search algorithm
KW - Dynamic population
KW - Exploration and exploitation
KW - Metaheuristics
UR - http://www.scopus.com/inward/record.url?scp=85124644947&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-95630-1_16
DO - 10.1007/978-3-030-95630-1_16
M3 - Conference contribution
AN - SCOPUS:85124644947
SN - 9783030956295
T3 - Communications in Computer and Information Science
SP - 227
EP - 239
BT - Informatics and Intelligent Applications - 1st International Conference, ICIIA 2021, Revised Selected Papers
A2 - Misra, Sanjay
A2 - Oluranti, Jonathan
A2 - Damaševičius, Robertas
A2 - Maskeliunas, Rytis
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 25 November 2021 through 27 November 2021
ER -