SFDE: Shuffled Frog-Leaping Differential Evolution and Its Application on Cognitive Radio Throughput
Differential Evolution (abbreviation for DE) is showing many advantages in solving optimization problems, such as fast convergence, strong robustness, and so on. However, when DE faces a complex target space, the diversity of its population will degenerate in a small scope; even sometimes it is prem...
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| Veröffentlicht in: | Wireless communications and mobile computing 2019-01, Vol.2019 (2019), p.1-18 |
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| creator | Wang, Hongbo Tu, Xuyan Zhen, Xiaoxiao |
| description | Differential Evolution (abbreviation for DE) is showing many advantages in solving optimization problems, such as fast convergence, strong robustness, and so on. However, when DE faces a complex target space, the diversity of its population will degenerate in a small scope; even sometimes it is premature to fall into the local minimum. All things contend in beauty in the world; a Shuffled Frog Leaping Algorithm (abbreviation for SFLA) has a strong global ability; unfortunately, its convergence speed is also slow. In order to overcome the shortcoming, this article suggests a Shuffled Frog-leaping Differential Evolution (abbreviation for SFDE) algorithm in a cognitive radio network, which combines Differential Evolution with Shuffled Frog Leaping Algorithm. This proposed method hikes its local searching for a certain number of subgroups, and their individuals join together and share their mutual information among different subgroups, which improves the population diversity and achieves the purpose of fast global search during the whole Differential Evolution. The SFDE is examined by 20 well-known numerical benchmark functions, and those obtained results are compared with four other related algorithms. The experimental simulation in solving the problem of effective throughput optimization for cognitive users shows that the proposed SFDE is effective. |
| doi_str_mv | 10.1155/2019/2965061 |
| format | Article |
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However, when DE faces a complex target space, the diversity of its population will degenerate in a small scope; even sometimes it is premature to fall into the local minimum. All things contend in beauty in the world; a Shuffled Frog Leaping Algorithm (abbreviation for SFLA) has a strong global ability; unfortunately, its convergence speed is also slow. In order to overcome the shortcoming, this article suggests a Shuffled Frog-leaping Differential Evolution (abbreviation for SFDE) algorithm in a cognitive radio network, which combines Differential Evolution with Shuffled Frog Leaping Algorithm. This proposed method hikes its local searching for a certain number of subgroups, and their individuals join together and share their mutual information among different subgroups, which improves the population diversity and achieves the purpose of fast global search during the whole Differential Evolution. The SFDE is examined by 20 well-known numerical benchmark functions, and those obtained results are compared with four other related algorithms. The experimental simulation in solving the problem of effective throughput optimization for cognitive users shows that the proposed SFDE is effective.</description><identifier>ISSN: 1530-8669</identifier><identifier>EISSN: 1530-8677</identifier><identifier>DOI: 10.1155/2019/2965061</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Algorithms ; Cognitive radio ; Computer simulation ; Convergence ; Evolutionary algorithms ; Evolutionary computation ; Food ; Foraging behavior ; Mutation ; Optimization ; Population ; Robustness (mathematics) ; Subgroups</subject><ispartof>Wireless communications and mobile computing, 2019-01, Vol.2019 (2019), p.1-18</ispartof><rights>Copyright © 2019 Hongbo Wang et al.</rights><rights>Copyright © 2019 Hongbo Wang et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c360t-30ae5eaad28f32373083d71c436d70b97abd4f95767fde3a0482acce3d43220e3</citedby><cites>FETCH-LOGICAL-c360t-30ae5eaad28f32373083d71c436d70b97abd4f95767fde3a0482acce3d43220e3</cites><orcidid>0000-0002-5408-7549</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27906,27907</link.rule.ids></links><search><contributor>Yaacoub, Charles</contributor><contributor>Charles Yaacoub</contributor><creatorcontrib>Wang, Hongbo</creatorcontrib><creatorcontrib>Tu, Xuyan</creatorcontrib><creatorcontrib>Zhen, Xiaoxiao</creatorcontrib><title>SFDE: Shuffled Frog-Leaping Differential Evolution and Its Application on Cognitive Radio Throughput</title><title>Wireless communications and mobile computing</title><description>Differential Evolution (abbreviation for DE) is showing many advantages in solving optimization problems, such as fast convergence, strong robustness, and so on. 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The experimental simulation in solving the problem of effective throughput optimization for cognitive users shows that the proposed SFDE is effective.</description><subject>Algorithms</subject><subject>Cognitive radio</subject><subject>Computer simulation</subject><subject>Convergence</subject><subject>Evolutionary algorithms</subject><subject>Evolutionary computation</subject><subject>Food</subject><subject>Foraging behavior</subject><subject>Mutation</subject><subject>Optimization</subject><subject>Population</subject><subject>Robustness (mathematics)</subject><subject>Subgroups</subject><issn>1530-8669</issn><issn>1530-8677</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqF0N9LwzAQB_AgCs7pm88S8FHrLkmbtL6N_dDBQHDzuWRN0mXUpqbtxP_ezg59FA7uOD7cwRehawIPhETRiAJJRjThEXByggYkYhDEXIjT35kn5-iirncAwICSAVKr-XT2iFfb1phCKzz3Lg-WWla2zPHUGqO9LhsrCzzbu6JtrCuxLBVeNDUeV1VhM_mz62ri8tI2dq_xq1TW4fXWuzbfVm1zic6MLGp9dexD9DafrSfPwfLlaTEZL4OMcWgCBlJHWkpFY8MoEwxipgTJQsaVgE0i5EaFJokEF0ZpJiGMqcwyzVTIKAXNhui2v1t599Hqukl3rvVl9zKlIQhOY0h4p-57lXlX116btPL2XfqvlEB6yDE95Jgec-z4Xc-3tlTy0_6nb3qtO6ON_NOUUBCEfQNtFHuj</recordid><startdate>20190101</startdate><enddate>20190101</enddate><creator>Wang, Hongbo</creator><creator>Tu, Xuyan</creator><creator>Zhen, Xiaoxiao</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>7XB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-5408-7549</orcidid></search><sort><creationdate>20190101</creationdate><title>SFDE: Shuffled Frog-Leaping Differential Evolution and Its Application on Cognitive Radio Throughput</title><author>Wang, Hongbo ; 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However, when DE faces a complex target space, the diversity of its population will degenerate in a small scope; even sometimes it is premature to fall into the local minimum. All things contend in beauty in the world; a Shuffled Frog Leaping Algorithm (abbreviation for SFLA) has a strong global ability; unfortunately, its convergence speed is also slow. In order to overcome the shortcoming, this article suggests a Shuffled Frog-leaping Differential Evolution (abbreviation for SFDE) algorithm in a cognitive radio network, which combines Differential Evolution with Shuffled Frog Leaping Algorithm. This proposed method hikes its local searching for a certain number of subgroups, and their individuals join together and share their mutual information among different subgroups, which improves the population diversity and achieves the purpose of fast global search during the whole Differential Evolution. 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| source | Wiley Online Library Open Access; EZB-FREE-00999 freely available EZB journals; Alma/SFX Local Collection |
| subjects | Algorithms Cognitive radio Computer simulation Convergence Evolutionary algorithms Evolutionary computation Food Foraging behavior Mutation Optimization Population Robustness (mathematics) Subgroups |
| title | SFDE: Shuffled Frog-Leaping Differential Evolution and Its Application on Cognitive Radio Throughput |
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