Effect of Population Structures on Quantum-Inspired Evolutionary Algorithm
Quantum-inspired evolutionary algorithm (QEA) has been designed by integrating some quantum mechanical principles in the framework of evolutionary algorithms. They have been successfully employed as a computational technique in solving difficult optimization problems. It is well known that QEAs prov...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2014/976202 |
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| author | Nija Mani Gursaran Srivastava A. K. Sinha Ashish Mani |
| author_facet | Nija Mani Gursaran Srivastava A. K. Sinha Ashish Mani |
| author_sort | Nija Mani |
| collection | DOAJ |
| description | Quantum-inspired evolutionary algorithm (QEA) has been designed by integrating some quantum mechanical principles in the framework of evolutionary algorithms. They have been successfully employed as a computational technique in solving difficult optimization problems. It is well known that QEAs provide better balance between exploration and exploitation as compared to the conventional evolutionary algorithms. The population in QEA is evolved by variation operators, which move the Q-bit towards an attractor. A modification for improving the performance of QEA was proposed by changing the selection of attractors, namely, versatile QEA. The improvement attained by versatile QEA over QEA indicates the impact of population structure on the performance of QEA and motivates further investigation into employing fine-grained model. The QEA with fine-grained population model (FQEA) is similar to QEA with the exception that every individual is located in a unique position on a two-dimensional toroidal grid and has four neighbors amongst which it selects its attractor. Further, FQEA does not use migrations, which is employed by QEAs. This paper empirically investigates the effect of the three different population structures on the performance of QEA by solving well-known discrete benchmark optimization problems. |
| format | Article |
| id | doaj-art-b4236ae76302430099fe9d3c311c76ef |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-b4236ae76302430099fe9d3c311c76ef2025-02-03T07:26:09ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322014-01-01201410.1155/2014/976202976202Effect of Population Structures on Quantum-Inspired Evolutionary AlgorithmNija Mani0Gursaran Srivastava1A. K. Sinha2Ashish Mani3Department of Mathematics, Dayalbagh Educational Institute, Dayalbagh, Agra 282005, IndiaDepartment of Mathematics, Dayalbagh Educational Institute, Dayalbagh, Agra 282005, IndiaDepartment of Mathematics, Dayalbagh Educational Institute, Dayalbagh, Agra 282005, IndiaUSIC, Dayalbagh Educational Institute, Dayalbagh, Agra 282005, IndiaQuantum-inspired evolutionary algorithm (QEA) has been designed by integrating some quantum mechanical principles in the framework of evolutionary algorithms. They have been successfully employed as a computational technique in solving difficult optimization problems. It is well known that QEAs provide better balance between exploration and exploitation as compared to the conventional evolutionary algorithms. The population in QEA is evolved by variation operators, which move the Q-bit towards an attractor. A modification for improving the performance of QEA was proposed by changing the selection of attractors, namely, versatile QEA. The improvement attained by versatile QEA over QEA indicates the impact of population structure on the performance of QEA and motivates further investigation into employing fine-grained model. The QEA with fine-grained population model (FQEA) is similar to QEA with the exception that every individual is located in a unique position on a two-dimensional toroidal grid and has four neighbors amongst which it selects its attractor. Further, FQEA does not use migrations, which is employed by QEAs. This paper empirically investigates the effect of the three different population structures on the performance of QEA by solving well-known discrete benchmark optimization problems.http://dx.doi.org/10.1155/2014/976202 |
| spellingShingle | Nija Mani Gursaran Srivastava A. K. Sinha Ashish Mani Effect of Population Structures on Quantum-Inspired Evolutionary Algorithm Applied Computational Intelligence and Soft Computing |
| title | Effect of Population Structures on Quantum-Inspired Evolutionary Algorithm |
| title_full | Effect of Population Structures on Quantum-Inspired Evolutionary Algorithm |
| title_fullStr | Effect of Population Structures on Quantum-Inspired Evolutionary Algorithm |
| title_full_unstemmed | Effect of Population Structures on Quantum-Inspired Evolutionary Algorithm |
| title_short | Effect of Population Structures on Quantum-Inspired Evolutionary Algorithm |
| title_sort | effect of population structures on quantum inspired evolutionary algorithm |
| url | http://dx.doi.org/10.1155/2014/976202 |
| work_keys_str_mv | AT nijamani effectofpopulationstructuresonquantuminspiredevolutionaryalgorithm AT gursaransrivastava effectofpopulationstructuresonquantuminspiredevolutionaryalgorithm AT aksinha effectofpopulationstructuresonquantuminspiredevolutionaryalgorithm AT ashishmani effectofpopulationstructuresonquantuminspiredevolutionaryalgorithm |