A modified self-adaptive marine predators algorithm: framework and engineering applications

Abstract The application of metaheuristic algorithms is one of the most promising approaches for solving real-world problems. The marine predators algorithm (MPA) is a recently proposed population-based metaheuristic algorithm that has been proven to be competitive with other algorithms. Although th...
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Autor*in:	Fan, Qingsong [verfasserIn] Huang, Haisong Chen, Qipeng Yao, Liguo Yang, Kai Huang, Dong

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Optimization Marine predators algorithm Self-adaptive rules Logistic opposition-based learning

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: Engineering with computers - London : Springer, 1985, 38(2021), 4 vom: 01. März, Seite 3269-3294
Übergeordnetes Werk:	volume:38 ; year:2021 ; number:4 ; day:01 ; month:03 ; pages:3269-3294

Links:	Volltext

DOI / URN:	10.1007/s00366-021-01319-5

Katalog-ID:	SPR047744936

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allfields	10.1007/s00366-021-01319-5 doi (DE-627)SPR047744936 (SPR)s00366-021-01319-5-e DE-627 ger DE-627 rakwb eng Fan, Qingsong verfasserin aut A modified self-adaptive marine predators algorithm: framework and engineering applications 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021 Abstract The application of metaheuristic algorithms is one of the most promising approaches for solving real-world problems. The marine predators algorithm (MPA) is a recently proposed population-based metaheuristic algorithm that has been proven to be competitive with other algorithms. Although the MPA shows good performance compared with other algorithms, modifications are still necessary to improve its optimization performance. Therefore, this paper proposes a modified MPA (MMPA). First, a logistic opposition-based learning (LOBL) mechanism is put forward to improve the population diversity and generate more accurate solutions. Second, effective self-adaptive updating methods are introduced into the original MPA, such as proposing the new position-updating rule, inertia weight coefficient and nonlinear step size control parameter strategy. The validity of the MMPA is tested on 23 classical benchmark functions, CEC 2020 functions and four real-world problems. Furthermore, the proposed algorithm is also evaluated using high-dimensional (Dim = 100, 1000 and 2000) benchmark functions. The experimental results and two different statistical tests demonstrate that the MMPA exhibits superior performance, and that it is competitive with many state-of-the-art algorithms in terms of accuracy, convergence speed, and stability. Optimization (dpeaa)DE-He213 Marine predators algorithm (dpeaa)DE-He213 Self-adaptive rules (dpeaa)DE-He213 Logistic opposition-based learning (dpeaa)DE-He213 Huang, Haisong (orcid)0000-0002-3750-6537 aut Chen, Qipeng aut Yao, Liguo aut Yang, Kai aut Huang, Dong aut Enthalten in Engineering with computers London : Springer, 1985 38(2021), 4 vom: 01. März, Seite 3269-3294 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:38 year:2021 number:4 day:01 month:03 pages:3269-3294 https://dx.doi.org/10.1007/s00366-021-01319-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2021 4 01 03 3269-3294
spelling	10.1007/s00366-021-01319-5 doi (DE-627)SPR047744936 (SPR)s00366-021-01319-5-e DE-627 ger DE-627 rakwb eng Fan, Qingsong verfasserin aut A modified self-adaptive marine predators algorithm: framework and engineering applications 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021 Abstract The application of metaheuristic algorithms is one of the most promising approaches for solving real-world problems. The marine predators algorithm (MPA) is a recently proposed population-based metaheuristic algorithm that has been proven to be competitive with other algorithms. Although the MPA shows good performance compared with other algorithms, modifications are still necessary to improve its optimization performance. Therefore, this paper proposes a modified MPA (MMPA). First, a logistic opposition-based learning (LOBL) mechanism is put forward to improve the population diversity and generate more accurate solutions. Second, effective self-adaptive updating methods are introduced into the original MPA, such as proposing the new position-updating rule, inertia weight coefficient and nonlinear step size control parameter strategy. The validity of the MMPA is tested on 23 classical benchmark functions, CEC 2020 functions and four real-world problems. Furthermore, the proposed algorithm is also evaluated using high-dimensional (Dim = 100, 1000 and 2000) benchmark functions. The experimental results and two different statistical tests demonstrate that the MMPA exhibits superior performance, and that it is competitive with many state-of-the-art algorithms in terms of accuracy, convergence speed, and stability. Optimization (dpeaa)DE-He213 Marine predators algorithm (dpeaa)DE-He213 Self-adaptive rules (dpeaa)DE-He213 Logistic opposition-based learning (dpeaa)DE-He213 Huang, Haisong (orcid)0000-0002-3750-6537 aut Chen, Qipeng aut Yao, Liguo aut Yang, Kai aut Huang, Dong aut Enthalten in Engineering with computers London : Springer, 1985 38(2021), 4 vom: 01. März, Seite 3269-3294 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:38 year:2021 number:4 day:01 month:03 pages:3269-3294 https://dx.doi.org/10.1007/s00366-021-01319-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2021 4 01 03 3269-3294
allfields_unstemmed	10.1007/s00366-021-01319-5 doi (DE-627)SPR047744936 (SPR)s00366-021-01319-5-e DE-627 ger DE-627 rakwb eng Fan, Qingsong verfasserin aut A modified self-adaptive marine predators algorithm: framework and engineering applications 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021 Abstract The application of metaheuristic algorithms is one of the most promising approaches for solving real-world problems. The marine predators algorithm (MPA) is a recently proposed population-based metaheuristic algorithm that has been proven to be competitive with other algorithms. Although the MPA shows good performance compared with other algorithms, modifications are still necessary to improve its optimization performance. Therefore, this paper proposes a modified MPA (MMPA). First, a logistic opposition-based learning (LOBL) mechanism is put forward to improve the population diversity and generate more accurate solutions. Second, effective self-adaptive updating methods are introduced into the original MPA, such as proposing the new position-updating rule, inertia weight coefficient and nonlinear step size control parameter strategy. The validity of the MMPA is tested on 23 classical benchmark functions, CEC 2020 functions and four real-world problems. Furthermore, the proposed algorithm is also evaluated using high-dimensional (Dim = 100, 1000 and 2000) benchmark functions. The experimental results and two different statistical tests demonstrate that the MMPA exhibits superior performance, and that it is competitive with many state-of-the-art algorithms in terms of accuracy, convergence speed, and stability. Optimization (dpeaa)DE-He213 Marine predators algorithm (dpeaa)DE-He213 Self-adaptive rules (dpeaa)DE-He213 Logistic opposition-based learning (dpeaa)DE-He213 Huang, Haisong (orcid)0000-0002-3750-6537 aut Chen, Qipeng aut Yao, Liguo aut Yang, Kai aut Huang, Dong aut Enthalten in Engineering with computers London : Springer, 1985 38(2021), 4 vom: 01. März, Seite 3269-3294 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:38 year:2021 number:4 day:01 month:03 pages:3269-3294 https://dx.doi.org/10.1007/s00366-021-01319-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2021 4 01 03 3269-3294
allfieldsGer	10.1007/s00366-021-01319-5 doi (DE-627)SPR047744936 (SPR)s00366-021-01319-5-e DE-627 ger DE-627 rakwb eng Fan, Qingsong verfasserin aut A modified self-adaptive marine predators algorithm: framework and engineering applications 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021 Abstract The application of metaheuristic algorithms is one of the most promising approaches for solving real-world problems. The marine predators algorithm (MPA) is a recently proposed population-based metaheuristic algorithm that has been proven to be competitive with other algorithms. Although the MPA shows good performance compared with other algorithms, modifications are still necessary to improve its optimization performance. Therefore, this paper proposes a modified MPA (MMPA). First, a logistic opposition-based learning (LOBL) mechanism is put forward to improve the population diversity and generate more accurate solutions. Second, effective self-adaptive updating methods are introduced into the original MPA, such as proposing the new position-updating rule, inertia weight coefficient and nonlinear step size control parameter strategy. The validity of the MMPA is tested on 23 classical benchmark functions, CEC 2020 functions and four real-world problems. Furthermore, the proposed algorithm is also evaluated using high-dimensional (Dim = 100, 1000 and 2000) benchmark functions. The experimental results and two different statistical tests demonstrate that the MMPA exhibits superior performance, and that it is competitive with many state-of-the-art algorithms in terms of accuracy, convergence speed, and stability. Optimization (dpeaa)DE-He213 Marine predators algorithm (dpeaa)DE-He213 Self-adaptive rules (dpeaa)DE-He213 Logistic opposition-based learning (dpeaa)DE-He213 Huang, Haisong (orcid)0000-0002-3750-6537 aut Chen, Qipeng aut Yao, Liguo aut Yang, Kai aut Huang, Dong aut Enthalten in Engineering with computers London : Springer, 1985 38(2021), 4 vom: 01. März, Seite 3269-3294 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:38 year:2021 number:4 day:01 month:03 pages:3269-3294 https://dx.doi.org/10.1007/s00366-021-01319-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2021 4 01 03 3269-3294
allfieldsSound	10.1007/s00366-021-01319-5 doi (DE-627)SPR047744936 (SPR)s00366-021-01319-5-e DE-627 ger DE-627 rakwb eng Fan, Qingsong verfasserin aut A modified self-adaptive marine predators algorithm: framework and engineering applications 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021 Abstract The application of metaheuristic algorithms is one of the most promising approaches for solving real-world problems. The marine predators algorithm (MPA) is a recently proposed population-based metaheuristic algorithm that has been proven to be competitive with other algorithms. Although the MPA shows good performance compared with other algorithms, modifications are still necessary to improve its optimization performance. Therefore, this paper proposes a modified MPA (MMPA). First, a logistic opposition-based learning (LOBL) mechanism is put forward to improve the population diversity and generate more accurate solutions. Second, effective self-adaptive updating methods are introduced into the original MPA, such as proposing the new position-updating rule, inertia weight coefficient and nonlinear step size control parameter strategy. The validity of the MMPA is tested on 23 classical benchmark functions, CEC 2020 functions and four real-world problems. Furthermore, the proposed algorithm is also evaluated using high-dimensional (Dim = 100, 1000 and 2000) benchmark functions. The experimental results and two different statistical tests demonstrate that the MMPA exhibits superior performance, and that it is competitive with many state-of-the-art algorithms in terms of accuracy, convergence speed, and stability. Optimization (dpeaa)DE-He213 Marine predators algorithm (dpeaa)DE-He213 Self-adaptive rules (dpeaa)DE-He213 Logistic opposition-based learning (dpeaa)DE-He213 Huang, Haisong (orcid)0000-0002-3750-6537 aut Chen, Qipeng aut Yao, Liguo aut Yang, Kai aut Huang, Dong aut Enthalten in Engineering with computers London : Springer, 1985 38(2021), 4 vom: 01. März, Seite 3269-3294 (DE-627)253722551 (DE-600)1459031-1 1435-5663 nnns volume:38 year:2021 number:4 day:01 month:03 pages:3269-3294 https://dx.doi.org/10.1007/s00366-021-01319-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2021 4 01 03 3269-3294
language	English
source	Enthalten in Engineering with computers 38(2021), 4 vom: 01. März, Seite 3269-3294 volume:38 year:2021 number:4 day:01 month:03 pages:3269-3294
sourceStr	Enthalten in Engineering with computers 38(2021), 4 vom: 01. März, Seite 3269-3294 volume:38 year:2021 number:4 day:01 month:03 pages:3269-3294
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Optimization Marine predators algorithm Self-adaptive rules Logistic opposition-based learning
isfreeaccess_bool	false
container_title	Engineering with computers
authorswithroles_txt_mv	Fan, Qingsong @@aut@@ Huang, Haisong @@aut@@ Chen, Qipeng @@aut@@ Yao, Liguo @@aut@@ Yang, Kai @@aut@@ Huang, Dong @@aut@@
publishDateDaySort_date	2021-03-01T00:00:00Z
hierarchy_top_id	253722551
id	SPR047744936
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR047744936</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230509104500.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220803s2021 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00366-021-01319-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR047744936</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00366-021-01319-5-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Fan, Qingsong</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A modified self-adaptive marine predators algorithm: framework and engineering applications</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2021</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The application of metaheuristic algorithms is one of the most promising approaches for solving real-world problems. The marine predators algorithm (MPA) is a recently proposed population-based metaheuristic algorithm that has been proven to be competitive with other algorithms. Although the MPA shows good performance compared with other algorithms, modifications are still necessary to improve its optimization performance. Therefore, this paper proposes a modified MPA (MMPA). First, a logistic opposition-based learning (LOBL) mechanism is put forward to improve the population diversity and generate more accurate solutions. Second, effective self-adaptive updating methods are introduced into the original MPA, such as proposing the new position-updating rule, inertia weight coefficient and nonlinear step size control parameter strategy. The validity of the MMPA is tested on 23 classical benchmark functions, CEC 2020 functions and four real-world problems. Furthermore, the proposed algorithm is also evaluated using high-dimensional (Dim = 100, 1000 and 2000) benchmark functions. 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author	Fan, Qingsong
spellingShingle	Fan, Qingsong misc Optimization misc Marine predators algorithm misc Self-adaptive rules misc Logistic opposition-based learning A modified self-adaptive marine predators algorithm: framework and engineering applications
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topic_title	A modified self-adaptive marine predators algorithm: framework and engineering applications Optimization (dpeaa)DE-He213 Marine predators algorithm (dpeaa)DE-He213 Self-adaptive rules (dpeaa)DE-He213 Logistic opposition-based learning (dpeaa)DE-He213
topic	misc Optimization misc Marine predators algorithm misc Self-adaptive rules misc Logistic opposition-based learning
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title	A modified self-adaptive marine predators algorithm: framework and engineering applications
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title_sort	modified self-adaptive marine predators algorithm: framework and engineering applications
title_auth	A modified self-adaptive marine predators algorithm: framework and engineering applications
abstract	Abstract The application of metaheuristic algorithms is one of the most promising approaches for solving real-world problems. The marine predators algorithm (MPA) is a recently proposed population-based metaheuristic algorithm that has been proven to be competitive with other algorithms. Although the MPA shows good performance compared with other algorithms, modifications are still necessary to improve its optimization performance. Therefore, this paper proposes a modified MPA (MMPA). First, a logistic opposition-based learning (LOBL) mechanism is put forward to improve the population diversity and generate more accurate solutions. Second, effective self-adaptive updating methods are introduced into the original MPA, such as proposing the new position-updating rule, inertia weight coefficient and nonlinear step size control parameter strategy. The validity of the MMPA is tested on 23 classical benchmark functions, CEC 2020 functions and four real-world problems. Furthermore, the proposed algorithm is also evaluated using high-dimensional (Dim = 100, 1000 and 2000) benchmark functions. The experimental results and two different statistical tests demonstrate that the MMPA exhibits superior performance, and that it is competitive with many state-of-the-art algorithms in terms of accuracy, convergence speed, and stability. © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021
abstractGer	Abstract The application of metaheuristic algorithms is one of the most promising approaches for solving real-world problems. The marine predators algorithm (MPA) is a recently proposed population-based metaheuristic algorithm that has been proven to be competitive with other algorithms. Although the MPA shows good performance compared with other algorithms, modifications are still necessary to improve its optimization performance. Therefore, this paper proposes a modified MPA (MMPA). First, a logistic opposition-based learning (LOBL) mechanism is put forward to improve the population diversity and generate more accurate solutions. Second, effective self-adaptive updating methods are introduced into the original MPA, such as proposing the new position-updating rule, inertia weight coefficient and nonlinear step size control parameter strategy. The validity of the MMPA is tested on 23 classical benchmark functions, CEC 2020 functions and four real-world problems. Furthermore, the proposed algorithm is also evaluated using high-dimensional (Dim = 100, 1000 and 2000) benchmark functions. The experimental results and two different statistical tests demonstrate that the MMPA exhibits superior performance, and that it is competitive with many state-of-the-art algorithms in terms of accuracy, convergence speed, and stability. © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021
abstract_unstemmed	Abstract The application of metaheuristic algorithms is one of the most promising approaches for solving real-world problems. The marine predators algorithm (MPA) is a recently proposed population-based metaheuristic algorithm that has been proven to be competitive with other algorithms. Although the MPA shows good performance compared with other algorithms, modifications are still necessary to improve its optimization performance. Therefore, this paper proposes a modified MPA (MMPA). First, a logistic opposition-based learning (LOBL) mechanism is put forward to improve the population diversity and generate more accurate solutions. Second, effective self-adaptive updating methods are introduced into the original MPA, such as proposing the new position-updating rule, inertia weight coefficient and nonlinear step size control parameter strategy. The validity of the MMPA is tested on 23 classical benchmark functions, CEC 2020 functions and four real-world problems. Furthermore, the proposed algorithm is also evaluated using high-dimensional (Dim = 100, 1000 and 2000) benchmark functions. The experimental results and two different statistical tests demonstrate that the MMPA exhibits superior performance, and that it is competitive with many state-of-the-art algorithms in terms of accuracy, convergence speed, and stability. © The Author(s), under exclusive licence to Springer-Verlag London Ltd. part of Springer Nature 2021
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container_issue	4
title_short	A modified self-adaptive marine predators algorithm: framework and engineering applications
url	https://dx.doi.org/10.1007/s00366-021-01319-5
remote_bool	true
author2	Huang, Haisong Chen, Qipeng Yao, Liguo Yang, Kai Huang, Dong
author2Str	Huang, Haisong Chen, Qipeng Yao, Liguo Yang, Kai Huang, Dong
ppnlink	253722551
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doi_str	10.1007/s00366-021-01319-5
up_date	2024-07-03T14:42:47.348Z
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