Enhanced harmony search algorithm with circular region perturbation for global optimization problems
To improve the searching effectiveness of the harmony search (HS) algorithm, an enhanced harmony search algorithm with circular region perturbation (EHS_CRP) is proposed in this paper. In the EHS_CRP algorithm, a global and local dimension selection strategy is designed to accelerate the search spee...
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| Veröffentlicht in: | Applied intelligence (Dordrecht, Netherlands) Netherlands), 2020-03, Vol.50 (3), p.951-975 |
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| creator | Wu, Wenqiang Ouyang, Haibin Mohamed, Ali Wagdy Zhang, Chunliang Li, Steven |
| description | To improve the searching effectiveness of the harmony search (HS) algorithm, an enhanced harmony search algorithm with circular region perturbation (EHS_CRP) is proposed in this paper. In the EHS_CRP algorithm, a global and local dimension selection strategy is designed to accelerate the search speed of the algorithm. A selection learning operator based on the global and local mean level is proposed to improve the balance between exploration and exploitation. Circular region perturbation is employed to avoid the algorithm stagnation and get a better exploration region. To assess performance, the proposed algorithm is compared with 10 state-of-the-art swarm intelligent approaches in a large set of global optimization problems. The simulation results confirm that EHS_CRP has a significant advantage in terms of accuracy, convergence speed, stability and robustness. Moreover, EHS_CRP performs better than other tested methods in engineering design optimization problems. Thus, the EHS_CRP algorithm is a viable and reliable alternative for some difficult and multidimensional real-world problems. |
| doi_str_mv | 10.1007/s10489-019-01558-6 |
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In the EHS_CRP algorithm, a global and local dimension selection strategy is designed to accelerate the search speed of the algorithm. A selection learning operator based on the global and local mean level is proposed to improve the balance between exploration and exploitation. Circular region perturbation is employed to avoid the algorithm stagnation and get a better exploration region. To assess performance, the proposed algorithm is compared with 10 state-of-the-art swarm intelligent approaches in a large set of global optimization problems. The simulation results confirm that EHS_CRP has a significant advantage in terms of accuracy, convergence speed, stability and robustness. Moreover, EHS_CRP performs better than other tested methods in engineering design optimization problems. Thus, the EHS_CRP algorithm is a viable and reliable alternative for some difficult and multidimensional real-world problems.</description><identifier>ISSN: 0924-669X</identifier><identifier>EISSN: 1573-7497</identifier><identifier>DOI: 10.1007/s10489-019-01558-6</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Artificial Intelligence ; Circularity ; Computer Science ; Computer simulation ; Design engineering ; Design optimization ; Exploration ; Global optimization ; Machine learning ; Machines ; Manufacturing ; Mechanical Engineering ; Perturbation ; Processes ; Search algorithms</subject><ispartof>Applied intelligence (Dordrecht, Netherlands), 2020-03, Vol.50 (3), p.951-975</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>Applied Intelligence is a copyright of Springer, (2019). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c319t-61bf48ee425e40d64346b19987b5cac596e1ba80a3189be926405ba76214ddcc3</citedby><cites>FETCH-LOGICAL-c319t-61bf48ee425e40d64346b19987b5cac596e1ba80a3189be926405ba76214ddcc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10489-019-01558-6$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10489-019-01558-6$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids></links><search><creatorcontrib>Wu, Wenqiang</creatorcontrib><creatorcontrib>Ouyang, Haibin</creatorcontrib><creatorcontrib>Mohamed, Ali Wagdy</creatorcontrib><creatorcontrib>Zhang, Chunliang</creatorcontrib><creatorcontrib>Li, Steven</creatorcontrib><title>Enhanced harmony search algorithm with circular region perturbation for global optimization problems</title><title>Applied intelligence (Dordrecht, Netherlands)</title><addtitle>Appl Intell</addtitle><description>To improve the searching effectiveness of the harmony search (HS) algorithm, an enhanced harmony search algorithm with circular region perturbation (EHS_CRP) is proposed in this paper. In the EHS_CRP algorithm, a global and local dimension selection strategy is designed to accelerate the search speed of the algorithm. A selection learning operator based on the global and local mean level is proposed to improve the balance between exploration and exploitation. Circular region perturbation is employed to avoid the algorithm stagnation and get a better exploration region. To assess performance, the proposed algorithm is compared with 10 state-of-the-art swarm intelligent approaches in a large set of global optimization problems. The simulation results confirm that EHS_CRP has a significant advantage in terms of accuracy, convergence speed, stability and robustness. Moreover, EHS_CRP performs better than other tested methods in engineering design optimization problems. Thus, the EHS_CRP algorithm is a viable and reliable alternative for some difficult and multidimensional real-world problems.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Circularity</subject><subject>Computer Science</subject><subject>Computer simulation</subject><subject>Design engineering</subject><subject>Design optimization</subject><subject>Exploration</subject><subject>Global optimization</subject><subject>Machine learning</subject><subject>Machines</subject><subject>Manufacturing</subject><subject>Mechanical Engineering</subject><subject>Perturbation</subject><subject>Processes</subject><subject>Search algorithms</subject><issn>0924-669X</issn><issn>1573-7497</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kEtLxDAUhYMoOI7-AVcB19W8myxlGB8w4EbBXUjS9DG0TU1aZPz1dqzgzsW9l3s551z4ALjG6BYjlN8ljJhUGcLH4lxm4gSsMM9pljOVn4IVUoRlQqj3c3CR0h4hRCnCK1Bs-9r0zhewNrEL_QEmb6KroWmrEJux7uDn3KFroptaE2H0VRN6OPg4TtGa8biUIcKqDda0MAxj0zVfy32Iwba-S5fgrDRt8le_cw3eHravm6ds9_L4vLnfZY5iNWYC25JJ7xnhnqFCMMqExUrJ3HJnHFfCY2skMhRLZb0igiFuTS4IZkXhHF2DmyV3fvwx-TTqfZhiP7_UhHJJiBQCzyqyqFwMKUVf6iE2nYkHjZE-0tQLTT3T1D80tZhNdDGlWdxXPv5F_-P6Bot4ec4</recordid><startdate>20200301</startdate><enddate>20200301</enddate><creator>Wu, Wenqiang</creator><creator>Ouyang, Haibin</creator><creator>Mohamed, Ali Wagdy</creator><creator>Zhang, Chunliang</creator><creator>Li, Steven</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PSYQQ</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20200301</creationdate><title>Enhanced harmony search algorithm with circular region perturbation for global optimization problems</title><author>Wu, Wenqiang ; 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In the EHS_CRP algorithm, a global and local dimension selection strategy is designed to accelerate the search speed of the algorithm. A selection learning operator based on the global and local mean level is proposed to improve the balance between exploration and exploitation. Circular region perturbation is employed to avoid the algorithm stagnation and get a better exploration region. To assess performance, the proposed algorithm is compared with 10 state-of-the-art swarm intelligent approaches in a large set of global optimization problems. The simulation results confirm that EHS_CRP has a significant advantage in terms of accuracy, convergence speed, stability and robustness. Moreover, EHS_CRP performs better than other tested methods in engineering design optimization problems. 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| subjects | Algorithms Artificial Intelligence Circularity Computer Science Computer simulation Design engineering Design optimization Exploration Global optimization Machine learning Machines Manufacturing Mechanical Engineering Perturbation Processes Search algorithms |
| title | Enhanced harmony search algorithm with circular region perturbation for global optimization problems |
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