Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP
Laplacian Biogeography-Based Optimization (LxBBO) is a BBO variant which improves BBO’s performance largely. When it solves some complex problems, however, it has some drawbacks such as poor performance, weak operability, and high complexity, so an improved LxBBO (ILxBBO) is proposed. First, a two-g...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/7824785 |
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| _version_ | 1832552107229577216 |
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| author | Xinming Zhang Doudou Wang Haiyan Chen Wentao Mao Shangwang Liu Guoqi Liu Zhi Dou |
| author_facet | Xinming Zhang Doudou Wang Haiyan Chen Wentao Mao Shangwang Liu Guoqi Liu Zhi Dou |
| author_sort | Xinming Zhang |
| collection | DOAJ |
| description | Laplacian Biogeography-Based Optimization (LxBBO) is a BBO variant which improves BBO’s performance largely. When it solves some complex problems, however, it has some drawbacks such as poor performance, weak operability, and high complexity, so an improved LxBBO (ILxBBO) is proposed. First, a two-global-best guiding operator is created for guiding the worst habitat mainly to enhance the exploitation of LxBBO. Second, a dynamic two-differential perturbing operator is proposed for the first two best habitats’ updating to improve the global search ability in the early search phase and the local one in the late search one, respectively. Third, an improved Laplace migration operator is formulated for other habitats’ updating to improve the search ability and the operability. Finally, some measures such as example learning, mutation operation removing, and greedy selection are adopted mostly to reduce the computation complexity of LxBBO. A lot of experimental results on the complex functions from the CEC-2013 test set show ILxBBO obtains better performance than LxBBO and quite a few state-of-the-art algorithms do. Also, the results on Quadratic Assignment Problems (QAPs) show that ILxBBO is more competitive compared with LxBBO, Improved Particle Swarm Optimization (IPSO), and Improved Firefly Algorithm (IFA). |
| format | Article |
| id | doaj-art-fd2da0d8e2354719872c8938dbdb3270 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-fd2da0d8e2354719872c8938dbdb32702025-02-03T05:59:35ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/78247857824785Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAPXinming Zhang0Doudou Wang1Haiyan Chen2Wentao Mao3Shangwang Liu4Guoqi Liu5Zhi Dou6College of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan 453007, ChinaCollege of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan 453007, ChinaDepartment of Gynecological Tumor, Hubei Cancer Hospital, Wuhan, Hubei 430079, ChinaCollege of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan 453007, ChinaCollege of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan 453007, ChinaCollege of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan 453007, ChinaCollege of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan 453007, ChinaLaplacian Biogeography-Based Optimization (LxBBO) is a BBO variant which improves BBO’s performance largely. When it solves some complex problems, however, it has some drawbacks such as poor performance, weak operability, and high complexity, so an improved LxBBO (ILxBBO) is proposed. First, a two-global-best guiding operator is created for guiding the worst habitat mainly to enhance the exploitation of LxBBO. Second, a dynamic two-differential perturbing operator is proposed for the first two best habitats’ updating to improve the global search ability in the early search phase and the local one in the late search one, respectively. Third, an improved Laplace migration operator is formulated for other habitats’ updating to improve the search ability and the operability. Finally, some measures such as example learning, mutation operation removing, and greedy selection are adopted mostly to reduce the computation complexity of LxBBO. A lot of experimental results on the complex functions from the CEC-2013 test set show ILxBBO obtains better performance than LxBBO and quite a few state-of-the-art algorithms do. Also, the results on Quadratic Assignment Problems (QAPs) show that ILxBBO is more competitive compared with LxBBO, Improved Particle Swarm Optimization (IPSO), and Improved Firefly Algorithm (IFA).http://dx.doi.org/10.1155/2020/7824785 |
| spellingShingle | Xinming Zhang Doudou Wang Haiyan Chen Wentao Mao Shangwang Liu Guoqi Liu Zhi Dou Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP Complexity |
| title | Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP |
| title_full | Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP |
| title_fullStr | Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP |
| title_full_unstemmed | Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP |
| title_short | Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP |
| title_sort | improved laplacian biogeography based optimization algorithm and its application to qap |
| url | http://dx.doi.org/10.1155/2020/7824785 |
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