Improvement and Application of Adaptive Hybrid Cuckoo Search Algorithm
Aiming at the problem of ease of falling into local optimum and low solution quality when solving optimization problems, this paper proposes an adaptive hybrid cuckoo search (AHCS) algorithm. AHCS improves the Lévy flight method and population evolution strategy of the cuckoo search (CS) algorithm,...
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| Veröffentlicht in: | IEEE access 2019, Vol.7, p.145489-145515 |
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| creator | Cheng, Zhiwen Wang, Jiquan Zhang, Mingxin Song, Haohao Chang, Tiezhu Bi, Yusheng Sun, Kexin |
| description | Aiming at the problem of ease of falling into local optimum and low solution quality when solving optimization problems, this paper proposes an adaptive hybrid cuckoo search (AHCS) algorithm. AHCS improves the Lévy flight method and population evolution strategy of the cuckoo search (CS) algorithm, and introduces a mutation operation operator. Inspired by the idea of position update of particle swarm optimization (PSO) algorithm, this paper introduces the inertia weight w in the Lévy flight method of CS algorithm, and gives the new dynamic adjustment methods of parameters α and β respectively. In order to enhance the local search ability and optimization speed of the algorithm, this paper introduces the mutation operation operator, and presents a new evolution strategy of the hybrid cuckoo search algorithm. In addition, in order to verify the performance of AHCS, 30 benchmark functions and CEC 2017 optimization problems were selected. The calculation results of the 30 benchmark functions and CEC 2017 optimization problems show that compared with other algorithms, the number of winning cases of t-test values and the Friedman average ranking for AHCS are significantly better than other algorithms. Finally, AHCS and various intelligent optimization methods in the literature are used to optimize the structural parameters of the reducer and the cantilever beam. The optimization results show that the quality of AHCS solution is significantly better than other algorithms. |
| doi_str_mv | 10.1109/ACCESS.2019.2944981 |
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AHCS improves the Lévy flight method and population evolution strategy of the cuckoo search (CS) algorithm, and introduces a mutation operation operator. Inspired by the idea of position update of particle swarm optimization (PSO) algorithm, this paper introduces the inertia weight w in the Lévy flight method of CS algorithm, and gives the new dynamic adjustment methods of parameters α and β respectively. In order to enhance the local search ability and optimization speed of the algorithm, this paper introduces the mutation operation operator, and presents a new evolution strategy of the hybrid cuckoo search algorithm. In addition, in order to verify the performance of AHCS, 30 benchmark functions and CEC 2017 optimization problems were selected. The calculation results of the 30 benchmark functions and CEC 2017 optimization problems show that compared with other algorithms, the number of winning cases of t-test values and the Friedman average ranking for AHCS are significantly better than other algorithms. Finally, AHCS and various intelligent optimization methods in the literature are used to optimize the structural parameters of the reducer and the cantilever beam. The optimization results show that the quality of AHCS solution is significantly better than other algorithms.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2019.2944981</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Adaptive algorithms ; adaptive parameter adjustment ; Algorithms ; Benchmarks ; Cantilever beams ; Classification algorithms ; Convergence ; Evolution ; evolutionary strategy ; Heuristic algorithms ; Hybrid cuckoo search algorithm ; Mathematical analysis ; Mathematical model ; Mutation ; mutation operator ; Optimization ; Parameters ; Particle swarm optimization ; Search algorithms ; Sociology ; Statistics</subject><ispartof>IEEE access, 2019, Vol.7, p.145489-145515</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c408t-215d79b0058d1e1eb8b02f58ac247335e976992cc232b881e0d255fdc8542ce63</citedby><cites>FETCH-LOGICAL-c408t-215d79b0058d1e1eb8b02f58ac247335e976992cc232b881e0d255fdc8542ce63</cites><orcidid>0000-0002-8860-5535 ; 0000-0002-1498-2602</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8854797$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,864,2102,4024,27633,27923,27924,27925,54933</link.rule.ids></links><search><creatorcontrib>Cheng, Zhiwen</creatorcontrib><creatorcontrib>Wang, Jiquan</creatorcontrib><creatorcontrib>Zhang, Mingxin</creatorcontrib><creatorcontrib>Song, Haohao</creatorcontrib><creatorcontrib>Chang, Tiezhu</creatorcontrib><creatorcontrib>Bi, Yusheng</creatorcontrib><creatorcontrib>Sun, Kexin</creatorcontrib><title>Improvement and Application of Adaptive Hybrid Cuckoo Search Algorithm</title><title>IEEE access</title><addtitle>Access</addtitle><description>Aiming at the problem of ease of falling into local optimum and low solution quality when solving optimization problems, this paper proposes an adaptive hybrid cuckoo search (AHCS) algorithm. AHCS improves the Lévy flight method and population evolution strategy of the cuckoo search (CS) algorithm, and introduces a mutation operation operator. Inspired by the idea of position update of particle swarm optimization (PSO) algorithm, this paper introduces the inertia weight w in the Lévy flight method of CS algorithm, and gives the new dynamic adjustment methods of parameters α and β respectively. In order to enhance the local search ability and optimization speed of the algorithm, this paper introduces the mutation operation operator, and presents a new evolution strategy of the hybrid cuckoo search algorithm. In addition, in order to verify the performance of AHCS, 30 benchmark functions and CEC 2017 optimization problems were selected. The calculation results of the 30 benchmark functions and CEC 2017 optimization problems show that compared with other algorithms, the number of winning cases of t-test values and the Friedman average ranking for AHCS are significantly better than other algorithms. Finally, AHCS and various intelligent optimization methods in the literature are used to optimize the structural parameters of the reducer and the cantilever beam. The optimization results show that the quality of AHCS solution is significantly better than other algorithms.</description><subject>Adaptive algorithms</subject><subject>adaptive parameter adjustment</subject><subject>Algorithms</subject><subject>Benchmarks</subject><subject>Cantilever beams</subject><subject>Classification algorithms</subject><subject>Convergence</subject><subject>Evolution</subject><subject>evolutionary strategy</subject><subject>Heuristic algorithms</subject><subject>Hybrid cuckoo search algorithm</subject><subject>Mathematical analysis</subject><subject>Mathematical model</subject><subject>Mutation</subject><subject>mutation operator</subject><subject>Optimization</subject><subject>Parameters</subject><subject>Particle swarm optimization</subject><subject>Search algorithms</subject><subject>Sociology</subject><subject>Statistics</subject><issn>2169-3536</issn><issn>2169-3536</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><sourceid>DOA</sourceid><recordid>eNpNkE1rwkAQhkNpoWL9BV4CPcfuZ7J7DEGrIPRge142uxNdq9l0EwX_fWMj0rnMMMz7zswTRVOMZhgj-ZYXxXyzmRGE5YxIxqTAD9GI4FQmlNP08V_9HE3ado_6EH2LZ6NosTo2wZ_hCHUX69rGedMcnNGd83Xsqzi3uuncGeLlpQzOxsXJfHsfb0AHs4vzw9YH1-2OL9FTpQ8tTG55HH0t5p_FMll_vK-KfJ0YhkSXEMxtJkuEuLAYMJSiRKTiQhvCMko5yCyVkhhDKCmFwIAs4byyRnBGDKR0HK0GX-v1XjXBHXW4KK-d-mv4sFU6dM4cQKU05czSkkNGmKFVmWoqhGAMS4q4KXuv18GrB_BzgrZTe38KdX--IozzFAtOaD9FhykTfNsGqO5bMVJX_mrgr6781Y1_r5oOKgcAd4Xo38hkRn8BbY1_Bg</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Cheng, Zhiwen</creator><creator>Wang, Jiquan</creator><creator>Zhang, Mingxin</creator><creator>Song, Haohao</creator><creator>Chang, Tiezhu</creator><creator>Bi, Yusheng</creator><creator>Sun, Kexin</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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AHCS improves the Lévy flight method and population evolution strategy of the cuckoo search (CS) algorithm, and introduces a mutation operation operator. Inspired by the idea of position update of particle swarm optimization (PSO) algorithm, this paper introduces the inertia weight w in the Lévy flight method of CS algorithm, and gives the new dynamic adjustment methods of parameters α and β respectively. In order to enhance the local search ability and optimization speed of the algorithm, this paper introduces the mutation operation operator, and presents a new evolution strategy of the hybrid cuckoo search algorithm. In addition, in order to verify the performance of AHCS, 30 benchmark functions and CEC 2017 optimization problems were selected. The calculation results of the 30 benchmark functions and CEC 2017 optimization problems show that compared with other algorithms, the number of winning cases of t-test values and the Friedman average ranking for AHCS are significantly better than other algorithms. Finally, AHCS and various intelligent optimization methods in the literature are used to optimize the structural parameters of the reducer and the cantilever beam. The optimization results show that the quality of AHCS solution is significantly better than other algorithms.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2019.2944981</doi><tpages>27</tpages><orcidid>https://orcid.org/0000-0002-8860-5535</orcidid><orcidid>https://orcid.org/0000-0002-1498-2602</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adaptive algorithms adaptive parameter adjustment Algorithms Benchmarks Cantilever beams Classification algorithms Convergence Evolution evolutionary strategy Heuristic algorithms Hybrid cuckoo search algorithm Mathematical analysis Mathematical model Mutation mutation operator Optimization Parameters Particle swarm optimization Search algorithms Sociology Statistics |
| title | Improvement and Application of Adaptive Hybrid Cuckoo Search Algorithm |
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