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.
الكلمات المفتاحية :
Quantum-inspired evolutionary algorithms
Local search
Continuous optimization
Exploration
Exploitation
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 -