A Parallel Compact Gannet Optimization Algorithm for Solving Engineering Optimization Problems
The Gannet Optimization Algorithm (GOA) has good performance, but there is still room for improvement in memory consumption and convergence. In this paper, an improved Gannet Optimization Algorithm is proposed to solve five engineering optimization problems. The compact strategy enables the GOA to s...
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| Veröffentlicht in: | Mathematics (Basel) Jg. 11; H. 2; S. 439 |
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| Abstract | The Gannet Optimization Algorithm (GOA) has good performance, but there is still room for improvement in memory consumption and convergence. In this paper, an improved Gannet Optimization Algorithm is proposed to solve five engineering optimization problems. The compact strategy enables the GOA to save a large amount of memory, and the parallel communication strategy allows the algorithm to avoid falling into local optimal solutions. We improve the GOA through the combination of parallel strategy and compact strategy, and we name the improved algorithm Parallel Compact Gannet Optimization Algorithm (PCGOA). The performance study of the PCGOA on the CEC2013 benchmark demonstrates the advantages of our new method in various aspects. Finally, the results of the PCGOA on solving five engineering optimization problems show that the improved algorithm can find the global optimal solution more accurately. |
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| AbstractList | The Gannet Optimization Algorithm (GOA) has good performance, but there is still room for improvement in memory consumption and convergence. In this paper, an improved Gannet Optimization Algorithm is proposed to solve five engineering optimization problems. The compact strategy enables the GOA to save a large amount of memory, and the parallel communication strategy allows the algorithm to avoid falling into local optimal solutions. We improve the GOA through the combination of parallel strategy and compact strategy, and we name the improved algorithm Parallel Compact Gannet Optimization Algorithm (PCGOA). The performance study of the PCGOA on the CEC2013 benchmark demonstrates the advantages of our new method in various aspects. Finally, the results of the PCGOA on solving five engineering optimization problems show that the improved algorithm can find the global optimal solution more accurately. |
| Author | Zhu, Minghui Sun, Bing Chu, Shu-Chuan Shieh, Chin-Shiuh Pan, Jeng-Shyang |
| Author_xml | – sequence: 1 givenname: Jeng-Shyang orcidid: 0000-0002-3128-9025 surname: Pan fullname: Pan, Jeng-Shyang – sequence: 2 givenname: Bing orcidid: 0000-0002-0001-2859 surname: Sun fullname: Sun, Bing – sequence: 3 givenname: Shu-Chuan orcidid: 0000-0003-2117-0618 surname: Chu fullname: Chu, Shu-Chuan – sequence: 4 givenname: Minghui surname: Zhu fullname: Zhu, Minghui – sequence: 5 givenname: Chin-Shiuh orcidid: 0000-0003-3187-458X surname: Shieh fullname: Shieh, Chin-Shiuh |
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| Cites_doi | 10.1016/j.matcom.2022.06.007 10.1109/NABIC.2009.5393690 10.1201/9781420036114 10.1016/j.knosys.2022.108124 10.1016/j.chemolab.2015.08.020 10.1109/MCI.2006.329691 10.1016/j.knosys.2021.106939 10.1007/s00500-016-2474-6 10.1007/978-3-642-32894-7_27 10.1016/j.eswa.2018.10.045 10.1007/s00500-017-2547-1 10.1007/s10462-018-9624-4 10.1016/j.cie.2021.107250 10.1109/TEVC.2010.2058120 10.1109/ACCESS.2020.2973411 10.1016/j.engappai.2019.06.017 10.1090/S0025-5718-1969-0247736-4 10.1109/TEVC.2002.802452 10.1080/0305215X.2013.832237 10.1016/j.engappai.2019.01.001 10.1007/s13369-018-03713-6 10.1007/s40747-021-00402-0 10.1007/s00500-018-3102-4 10.1007/s00158-009-0454-5 10.1016/j.compstruc.2014.03.007 10.1016/j.knosys.2021.107218 10.1016/j.ins.2020.11.056 10.1137/1.9781611974331.ch9 10.3390/app9101973 10.1007/s001580100117 10.1016/j.engappai.2020.104049 10.1016/j.knosys.2015.12.022 10.1016/j.ins.2013.02.041 10.1109/ACCESS.2019.2921721 10.1007/s00366-011-0241-y 10.1007/3-540-58484-6_285 10.1016/j.aci.2017.09.001 10.1109/4235.797971 |
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