Article
Journal/Revue :
Applied Soft Computing
ISSN : 1568-4946
Publisher :
Informations
Période : December 2017
Volume : 61
Pages : 765-791
Détails
A new real-coded quantum-inspired evolutionary algorithm for continuous optimization
Hichem Talbi Amer Draa
This paper presents a recursive deepening hybrid strategy to solve real-parameter optimization problems. It couples a local search technique with a quantum-inspired evolutionary algorithm. In order to adapt the quantum-inspired evolutionary algorithm for continuous optimization without losing the states superposition property, a suitable sampling of the search space that tightens recursively and an integration of a uniformly generated random part after measurement have been utilized. The use of local search provides, for each search window, a good exploitation of the quantum inspired generated solution's neighbourhood. The proposed approach has been tested through the reference black-box optimization benchmarking framework. The comparison of the obtained results with those of some state-of-the-art algorithms has shown its actual effectiveness.
Mots clés :
Quantum-inspired evolutionary algorithms Local search Continuous optimization Exploration Exploitation
Réf. de citation :
misc-lab-131
DOI :
10.1016/j.asoc.2017.07.046
Lien :
Texte intégral
ACM :
H. Talbi and A. Draa. 2017. A new real-coded quantum-inspired evolutionary algorithm for continuous optimization. Applied Soft Computing, 61 (December 2017), Elsevier, 765-791. DOI: https://doi.org/10.1016/j.asoc.2017.07.046.
APA :
Talbi, H. & Draa, A. (2017, December). A new real-coded quantum-inspired evolutionary algorithm for continuous optimization. Applied Soft Computing, 61, Elsevier, 765-791. DOI: https://doi.org/10.1016/j.asoc.2017.07.046
IEEE :
H. Talbi and A. Draa, "A new real-coded quantum-inspired evolutionary algorithm for continuous optimization". Applied Soft Computing, vol. 61, Elsevier, pp. 765-791, December, 2017. DOI: https://doi.org/10.1016/j.asoc.2017.07.046.
BibTeX :
@article{misc-lab-131,
author = {Talbi, Hichem and Draa, Amer},
title = {A new real-coded quantum-inspired evolutionary algorithm for continuous optimization},
journal = {Applied Soft Computing},
volume = {61},
issn = {1568-4946},
pages = {765--791},
publisher = {Elsevier},
year = {2017},
month = {December},
doi = {10.1016/j.asoc.2017.07.046},
url = {https://www.sciencedirect.com/science/article/pii/S1568494617304660?via%3Dihub},
keywords = {Quantum-inspired evolutionary algorithms, Local search, Continuous optimization, Exploration, Exploitation}
}
RIS :
TI  - A new real-coded quantum-inspired evolutionary algorithm for continuous optimization
AU - H. Talbi
AU - A. Draa
PY - 2017
SN - 1568-4946
JO - Applied Soft Computing
VL - 61
SP - 765
EP - 791
PB - Elsevier
AB - This paper presents a recursive deepening hybrid strategy to solve real-parameter optimization problems. It couples a local search technique with a quantum-inspired evolutionary algorithm. In order to adapt the quantum-inspired evolutionary algorithm for continuous optimization without losing the states superposition property, a suitable sampling of the search space that tightens recursively and an integration of a uniformly generated random part after measurement have been utilized. The use of local search provides, for each search window, a good exploitation of the quantum inspired generated solution's neighbourhood. The proposed approach has been tested through the reference black-box optimization benchmarking framework. The comparison of the obtained results with those of some state-of-the-art algorithms has shown its actual effectiveness.
KW - Quantum-inspired evolutionary algorithms
KW - Local search
KW - Continuous optimization
KW - Exploration
KW - Exploitation
DO - 10.1016/j.asoc.2017.07.046
UR - https://www.sciencedirect.com/science/article/pii/S1568494617304660?via%3Dihub
ID - misc-lab-131
ER -