Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function
Abstract Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican’s natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Song, Hao-Ming [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2023 |
|---|
| Schlagwörter: |
Pelican optimization algorithm |
|---|
| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
|---|
| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 27(2023), 15 vom: 26. Apr., Seite 10607-10646 |
|---|---|
| Übergeordnetes Werk: |
volume:27 ; year:2023 ; number:15 ; day:26 ; month:04 ; pages:10607-10646 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00500-023-08205-w |
|---|
| Katalog-ID: |
SPR051920573 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR051920573 | ||
| 003 | DE-627 | ||
| 005 | 20230617064743.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230617s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s00500-023-08205-w |2 doi | |
| 035 | |a (DE-627)SPR051920573 | ||
| 035 | |a (SPR)s00500-023-08205-w-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Song, Hao-Ming |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. | ||
| 520 | |a Abstract Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican’s natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications. | ||
| 650 | 4 | |a Pelican optimization algorithm |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Chaotic mapping |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Elementary mathematical function |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Function optimization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Engineering optimization |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Xing, Cheng |4 aut | |
| 700 | 1 | |a Wang, Jie-Sheng |4 aut | |
| 700 | 1 | |a Wang, Yu-Cai |4 aut | |
| 700 | 1 | |a Liu, Yu |4 aut | |
| 700 | 1 | |a Zhu, Jun-Hua |4 aut | |
| 700 | 1 | |a Hou, Jia-Ning |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Soft Computing |d Springer-Verlag, 2003 |g 27(2023), 15 vom: 26. Apr., Seite 10607-10646 |w (DE-627)SPR006469531 |7 nnns |
| 773 | 1 | 8 | |g volume:27 |g year:2023 |g number:15 |g day:26 |g month:04 |g pages:10607-10646 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s00500-023-08205-w |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 951 | |a AR | ||
| 952 | |d 27 |j 2023 |e 15 |b 26 |c 04 |h 10607-10646 | ||
| author_variant |
h m s hms c x cx j s w jsw y c w ycw y l yl j h z jhz j n h jnh |
|---|---|
| matchkey_str |
songhaomingxingchengwangjieshengwangyuca:2023----:mrvdeiaotmztoagrtmihhoiitreecfcoadlm |
| hierarchy_sort_str |
2023 |
| publishDate |
2023 |
| allfields |
10.1007/s00500-023-08205-w doi (DE-627)SPR051920573 (SPR)s00500-023-08205-w-e DE-627 ger DE-627 rakwb eng Song, Hao-Ming verfasserin aut Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican’s natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications. Pelican optimization algorithm (dpeaa)DE-He213 Chaotic mapping (dpeaa)DE-He213 Elementary mathematical function (dpeaa)DE-He213 Function optimization (dpeaa)DE-He213 Engineering optimization (dpeaa)DE-He213 Xing, Cheng aut Wang, Jie-Sheng aut Wang, Yu-Cai aut Liu, Yu aut Zhu, Jun-Hua aut Hou, Jia-Ning aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 15 vom: 26. Apr., Seite 10607-10646 (DE-627)SPR006469531 nnns volume:27 year:2023 number:15 day:26 month:04 pages:10607-10646 https://dx.doi.org/10.1007/s00500-023-08205-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 15 26 04 10607-10646 |
| spelling |
10.1007/s00500-023-08205-w doi (DE-627)SPR051920573 (SPR)s00500-023-08205-w-e DE-627 ger DE-627 rakwb eng Song, Hao-Ming verfasserin aut Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican’s natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications. Pelican optimization algorithm (dpeaa)DE-He213 Chaotic mapping (dpeaa)DE-He213 Elementary mathematical function (dpeaa)DE-He213 Function optimization (dpeaa)DE-He213 Engineering optimization (dpeaa)DE-He213 Xing, Cheng aut Wang, Jie-Sheng aut Wang, Yu-Cai aut Liu, Yu aut Zhu, Jun-Hua aut Hou, Jia-Ning aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 15 vom: 26. Apr., Seite 10607-10646 (DE-627)SPR006469531 nnns volume:27 year:2023 number:15 day:26 month:04 pages:10607-10646 https://dx.doi.org/10.1007/s00500-023-08205-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 15 26 04 10607-10646 |
| allfields_unstemmed |
10.1007/s00500-023-08205-w doi (DE-627)SPR051920573 (SPR)s00500-023-08205-w-e DE-627 ger DE-627 rakwb eng Song, Hao-Ming verfasserin aut Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican’s natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications. Pelican optimization algorithm (dpeaa)DE-He213 Chaotic mapping (dpeaa)DE-He213 Elementary mathematical function (dpeaa)DE-He213 Function optimization (dpeaa)DE-He213 Engineering optimization (dpeaa)DE-He213 Xing, Cheng aut Wang, Jie-Sheng aut Wang, Yu-Cai aut Liu, Yu aut Zhu, Jun-Hua aut Hou, Jia-Ning aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 15 vom: 26. Apr., Seite 10607-10646 (DE-627)SPR006469531 nnns volume:27 year:2023 number:15 day:26 month:04 pages:10607-10646 https://dx.doi.org/10.1007/s00500-023-08205-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 15 26 04 10607-10646 |
| allfieldsGer |
10.1007/s00500-023-08205-w doi (DE-627)SPR051920573 (SPR)s00500-023-08205-w-e DE-627 ger DE-627 rakwb eng Song, Hao-Ming verfasserin aut Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican’s natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications. Pelican optimization algorithm (dpeaa)DE-He213 Chaotic mapping (dpeaa)DE-He213 Elementary mathematical function (dpeaa)DE-He213 Function optimization (dpeaa)DE-He213 Engineering optimization (dpeaa)DE-He213 Xing, Cheng aut Wang, Jie-Sheng aut Wang, Yu-Cai aut Liu, Yu aut Zhu, Jun-Hua aut Hou, Jia-Ning aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 15 vom: 26. Apr., Seite 10607-10646 (DE-627)SPR006469531 nnns volume:27 year:2023 number:15 day:26 month:04 pages:10607-10646 https://dx.doi.org/10.1007/s00500-023-08205-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 15 26 04 10607-10646 |
| allfieldsSound |
10.1007/s00500-023-08205-w doi (DE-627)SPR051920573 (SPR)s00500-023-08205-w-e DE-627 ger DE-627 rakwb eng Song, Hao-Ming verfasserin aut Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican’s natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications. Pelican optimization algorithm (dpeaa)DE-He213 Chaotic mapping (dpeaa)DE-He213 Elementary mathematical function (dpeaa)DE-He213 Function optimization (dpeaa)DE-He213 Engineering optimization (dpeaa)DE-He213 Xing, Cheng aut Wang, Jie-Sheng aut Wang, Yu-Cai aut Liu, Yu aut Zhu, Jun-Hua aut Hou, Jia-Ning aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 15 vom: 26. Apr., Seite 10607-10646 (DE-627)SPR006469531 nnns volume:27 year:2023 number:15 day:26 month:04 pages:10607-10646 https://dx.doi.org/10.1007/s00500-023-08205-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 15 26 04 10607-10646 |
| language |
English |
| source |
Enthalten in Soft Computing 27(2023), 15 vom: 26. Apr., Seite 10607-10646 volume:27 year:2023 number:15 day:26 month:04 pages:10607-10646 |
| sourceStr |
Enthalten in Soft Computing 27(2023), 15 vom: 26. Apr., Seite 10607-10646 volume:27 year:2023 number:15 day:26 month:04 pages:10607-10646 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Pelican optimization algorithm Chaotic mapping Elementary mathematical function Function optimization Engineering optimization |
| isfreeaccess_bool |
false |
| container_title |
Soft Computing |
| authorswithroles_txt_mv |
Song, Hao-Ming @@aut@@ Xing, Cheng @@aut@@ Wang, Jie-Sheng @@aut@@ Wang, Yu-Cai @@aut@@ Liu, Yu @@aut@@ Zhu, Jun-Hua @@aut@@ Hou, Jia-Ning @@aut@@ |
| publishDateDaySort_date |
2023-04-26T00:00:00Z |
| hierarchy_top_id |
SPR006469531 |
| id |
SPR051920573 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR051920573</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230617064743.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230617s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-023-08205-w</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR051920573</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-023-08205-w-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">Song, Hao-Ming</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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 GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican’s natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pelican optimization algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Chaotic mapping</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Elementary mathematical function</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Function optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Engineering optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xing, Cheng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Jie-Sheng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Yu-Cai</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Yu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Jun-Hua</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hou, Jia-Ning</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">27(2023), 15 vom: 26. Apr., Seite 10607-10646</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:27</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:15</subfield><subfield code="g">day:26</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:10607-10646</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-023-08205-w</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">27</subfield><subfield code="j">2023</subfield><subfield code="e">15</subfield><subfield code="b">26</subfield><subfield code="c">04</subfield><subfield code="h">10607-10646</subfield></datafield></record></collection>
|
| author |
Song, Hao-Ming |
| spellingShingle |
Song, Hao-Ming misc Pelican optimization algorithm misc Chaotic mapping misc Elementary mathematical function misc Function optimization misc Engineering optimization Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function |
| authorStr |
Song, Hao-Ming |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)SPR006469531 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| topic_title |
Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function Pelican optimization algorithm (dpeaa)DE-He213 Chaotic mapping (dpeaa)DE-He213 Elementary mathematical function (dpeaa)DE-He213 Function optimization (dpeaa)DE-He213 Engineering optimization (dpeaa)DE-He213 |
| topic |
misc Pelican optimization algorithm misc Chaotic mapping misc Elementary mathematical function misc Function optimization misc Engineering optimization |
| topic_unstemmed |
misc Pelican optimization algorithm misc Chaotic mapping misc Elementary mathematical function misc Function optimization misc Engineering optimization |
| topic_browse |
misc Pelican optimization algorithm misc Chaotic mapping misc Elementary mathematical function misc Function optimization misc Engineering optimization |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Soft Computing |
| hierarchy_parent_id |
SPR006469531 |
| hierarchy_top_title |
Soft Computing |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)SPR006469531 |
| title |
Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function |
| ctrlnum |
(DE-627)SPR051920573 (SPR)s00500-023-08205-w-e |
| title_full |
Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function |
| author_sort |
Song, Hao-Ming |
| journal |
Soft Computing |
| journalStr |
Soft Computing |
| lang_code |
eng |
| isOA_bool |
false |
| recordtype |
marc |
| publishDateSort |
2023 |
| contenttype_str_mv |
txt |
| container_start_page |
10607 |
| author_browse |
Song, Hao-Ming Xing, Cheng Wang, Jie-Sheng Wang, Yu-Cai Liu, Yu Zhu, Jun-Hua Hou, Jia-Ning |
| container_volume |
27 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Song, Hao-Ming |
| doi_str_mv |
10.1007/s00500-023-08205-w |
| title_sort |
improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function |
| title_auth |
Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function |
| abstract |
Abstract Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican’s natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican’s natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican’s natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER |
| container_issue |
15 |
| title_short |
Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function |
| url |
https://dx.doi.org/10.1007/s00500-023-08205-w |
| remote_bool |
true |
| author2 |
Xing, Cheng Wang, Jie-Sheng Wang, Yu-Cai Liu, Yu Zhu, Jun-Hua Hou, Jia-Ning |
| author2Str |
Xing, Cheng Wang, Jie-Sheng Wang, Yu-Cai Liu, Yu Zhu, Jun-Hua Hou, Jia-Ning |
| ppnlink |
SPR006469531 |
| mediatype_str_mv |
c |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00500-023-08205-w |
| up_date |
2024-07-04T00:26:31.035Z |
| _version_ |
1803606075414413312 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR051920573</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230617064743.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230617s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-023-08205-w</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR051920573</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-023-08205-w-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">Song, Hao-Ming</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Improved pelican optimization algorithm with chaotic interference factor and elementary mathematical function</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</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 GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Pelican optimization algorithm (POA) is a new heuristic algorithm that simulates the pelican’s natural behavior in the hunting process. In order to improve the convergence speed and accuracy of the original algorithm and to solve the problem that the original algorithm is easy to fall into local optimization, an improved POA based on chaotic interference factor and elementary mathematical function is proposed. In this paper, ten different chaotic interference factors are introduced in the exploration stage of POA. After selecting an improved POA with the best performance, six different elementary mathematical functions are introduced in the exploitation stage of POA to improve its optimization performance. Then 30 benchmark functions in CEC-BC-2017 were used to test the performance of different improved algorithms. The experimental results showed that the performance of the improved algorithms have been improved effectively compared with the original POA, and the accuracy and optimization ability to balance exploration and exploitation were significantly improved. Compared with seven different algorithms, the feasibility of the improved POA proposed in this paper is proved. Finally, four engineering design problems are optimized, and the simulation results show that among four different engineering design problems, the improved POA proposed in this paper is obviously superior to the original POA, which proves that the improved POA based on chaotic interference factor and elementary function is competitive in optimization performance on function optimization and practical engineering applications.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Pelican optimization algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Chaotic mapping</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Elementary mathematical function</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Function optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Engineering optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xing, Cheng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Jie-Sheng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Yu-Cai</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Yu</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhu, Jun-Hua</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hou, Jia-Ning</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Soft Computing</subfield><subfield code="d">Springer-Verlag, 2003</subfield><subfield code="g">27(2023), 15 vom: 26. Apr., Seite 10607-10646</subfield><subfield code="w">(DE-627)SPR006469531</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:27</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:15</subfield><subfield code="g">day:26</subfield><subfield code="g">month:04</subfield><subfield code="g">pages:10607-10646</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-023-08205-w</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">27</subfield><subfield code="j">2023</subfield><subfield code="e">15</subfield><subfield code="b">26</subfield><subfield code="c">04</subfield><subfield code="h">10607-10646</subfield></datafield></record></collection>
|
| score |
7.4009047 |