Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism
The antibody candidate set generated by the clonal selection algorithm has only a small number of antibodies with high antigen affinity to obtain high-frequency mutations. Among other antibodies, some low-affinity antibodies are replaced by new antibodies to participate in the next clonal selection....
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/2807056 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832566249077342208 |
|---|---|
| author | Chao Yang Bing-qiu Chen Lin Jia Hai-yang Wen |
| author_facet | Chao Yang Bing-qiu Chen Lin Jia Hai-yang Wen |
| author_sort | Chao Yang |
| collection | DOAJ |
| description | The antibody candidate set generated by the clonal selection algorithm has only a small number of antibodies with high antigen affinity to obtain high-frequency mutations. Among other antibodies, some low-affinity antibodies are replaced by new antibodies to participate in the next clonal selection. A large number of antibodies with high affinity make it difficult to participate in clonal selection and exist in antibody concentration for a long time. This part of inactive antibody forms a “black hole” of the antibody set, which is difficult to remove and update in a timely manner, thus affecting the speed at which the algorithm approximates the optimal solution. Inspired by the mechanism of biological forgetting, an improved clonal selection algorithm is proposed to solve this problem. It aims to use the abstract mechanism of biological forgetting to eliminate antibodies that cannot actively participate in high-frequency mutations in the antibody candidate set and to improve the problem of insufficient diversity of antibodies in the clonal selection algorithm, which is prone to fall into the local optimal. Compared with the existing clonal selection and genetic algorithms, the experiment and time complexity analysis show that the algorithm has good optimization efficiency and stability. |
| format | Article |
| id | doaj-art-6d2dda05f5db4690bfef4e0590e9aaf6 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-6d2dda05f5db4690bfef4e0590e9aaf62025-02-03T01:04:40ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/28070562807056Improved Clonal Selection Algorithm Based on Biological Forgetting MechanismChao Yang0Bing-qiu Chen1Lin Jia2Hai-yang Wen3School of Computer Science and Information Engineering, Hubei University, Hubei Province, Wuhan 430062, ChinaSchool of Computer Science and Information Engineering, Hubei University, Hubei Province, Wuhan 430062, ChinaSchool of Computer Science and Information Engineering, Hubei University, Hubei Province, Wuhan 430062, ChinaSchool of Computer Science and Information Engineering, Hubei University, Hubei Province, Wuhan 430062, ChinaThe antibody candidate set generated by the clonal selection algorithm has only a small number of antibodies with high antigen affinity to obtain high-frequency mutations. Among other antibodies, some low-affinity antibodies are replaced by new antibodies to participate in the next clonal selection. A large number of antibodies with high affinity make it difficult to participate in clonal selection and exist in antibody concentration for a long time. This part of inactive antibody forms a “black hole” of the antibody set, which is difficult to remove and update in a timely manner, thus affecting the speed at which the algorithm approximates the optimal solution. Inspired by the mechanism of biological forgetting, an improved clonal selection algorithm is proposed to solve this problem. It aims to use the abstract mechanism of biological forgetting to eliminate antibodies that cannot actively participate in high-frequency mutations in the antibody candidate set and to improve the problem of insufficient diversity of antibodies in the clonal selection algorithm, which is prone to fall into the local optimal. Compared with the existing clonal selection and genetic algorithms, the experiment and time complexity analysis show that the algorithm has good optimization efficiency and stability.http://dx.doi.org/10.1155/2020/2807056 |
| spellingShingle | Chao Yang Bing-qiu Chen Lin Jia Hai-yang Wen Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism Complexity |
| title | Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism |
| title_full | Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism |
| title_fullStr | Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism |
| title_full_unstemmed | Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism |
| title_short | Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism |
| title_sort | improved clonal selection algorithm based on biological forgetting mechanism |
| url | http://dx.doi.org/10.1155/2020/2807056 |
| work_keys_str_mv | AT chaoyang improvedclonalselectionalgorithmbasedonbiologicalforgettingmechanism AT bingqiuchen improvedclonalselectionalgorithmbasedonbiologicalforgettingmechanism AT linjia improvedclonalselectionalgorithmbasedonbiologicalforgettingmechanism AT haiyangwen improvedclonalselectionalgorithmbasedonbiologicalforgettingmechanism |