Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection
Abstract The basic salp swarm algorithm (SSA) is a novel nature-inspired swarm intelligence optimization algorithm based on the foraging behavior of salp individuals in the deep sea. Since its development, the salp swarm algorithm has attracted widespread interest from scholars both at home and abro...
Ausführliche Beschreibung
Autor*in: |
Li, Jun [verfasserIn] |
---|
Format: |
E-Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2023 |
---|
Schlagwörter: |
---|
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), 23 vom: 18. Aug., Seite 17887-17908 |
---|---|
Übergeordnetes Werk: |
volume:27 ; year:2023 ; number:23 ; day:18 ; month:08 ; pages:17887-17908 |
Links: |
---|
DOI / URN: |
10.1007/s00500-023-09070-3 |
---|
Katalog-ID: |
SPR053395875 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | SPR053395875 | ||
003 | DE-627 | ||
005 | 20231014064635.0 | ||
007 | cr uuu---uuuuu | ||
008 | 231014s2023 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1007/s00500-023-09070-3 |2 doi | |
035 | |a (DE-627)SPR053395875 | ||
035 | |a (SPR)s00500-023-09070-3-e | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
100 | 1 | |a Li, Jun |e verfasserin |4 aut | |
245 | 1 | 0 | |a Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection |
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 The basic salp swarm algorithm (SSA) is a novel nature-inspired swarm intelligence optimization algorithm based on the foraging behavior of salp individuals in the deep sea. Since its development, the salp swarm algorithm has attracted widespread interest from scholars both at home and abroad for solving complex real-world practical problems. With continuous research, the SSA algorithm has revealed some shortcomings such as slow convergence speed and low accuracy. To enhance the optimization capability of the algorithm, in this paper, we propose an improved hybrid algorithm called TLSSA based on two phases of the teaching–learning-based optimization method: the teaching phase and the learner phase. In the teaching phase, students' ability is improved by updating the difference between the teacher and the class average level, which helps to improve the overall learning ability of the salp population, resulting in higher quality solutions. In the learning phase, by simulating the discussion, statement, and communication between students, the average level of the individual is improved, and the global search speed of the algorithm is accelerated. To verify the effectiveness and competitiveness of the proposed method, it is first tested on 30 IEEE CEC 2017 benchmark functions. The test results demonstrate that the proposed TLSSA method obtains better overall performance compared to 8 mainstream meta-heuristics and 8 advanced algorithms. After that, we applied the proposed method to solve two classical real-world engineering design problems and feature selection. Again, the experimental results show that our method has significant advantages over the traditional methods in solving these practical problems. The remarkable performance of our proposed improved TLSSA algorithm in solving theoretical and practical complex optimization problems also provides potential possibilities for applying more intelligent optimization algorithms to solve complex optimization problems in real-life situations in the future. | ||
650 | 4 | |a Salp swarm algorithm |7 (dpeaa)DE-He213 | |
650 | 4 | |a Teaching–learning-based optimization |7 (dpeaa)DE-He213 | |
650 | 4 | |a Engineering design |7 (dpeaa)DE-He213 | |
650 | 4 | |a Feature selection |7 (dpeaa)DE-He213 | |
700 | 1 | |a Ren, Hao |4 aut | |
700 | 1 | |a Chen, Huiling |4 aut | |
700 | 1 | |a Li, ChenYang |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Soft Computing |d Springer-Verlag, 2003 |g 27(2023), 23 vom: 18. Aug., Seite 17887-17908 |w (DE-627)SPR006469531 |7 nnns |
773 | 1 | 8 | |g volume:27 |g year:2023 |g number:23 |g day:18 |g month:08 |g pages:17887-17908 |
856 | 4 | 0 | |u https://dx.doi.org/10.1007/s00500-023-09070-3 |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 23 |b 18 |c 08 |h 17887-17908 |
author_variant |
j l jl h r hr h c hc c l cl |
---|---|
matchkey_str |
lijunrenhaochenhuilinglichenyang:2023----:ecigeriguddapwragrtmogoaotmztot |
hierarchy_sort_str |
2023 |
publishDate |
2023 |
allfields |
10.1007/s00500-023-09070-3 doi (DE-627)SPR053395875 (SPR)s00500-023-09070-3-e DE-627 ger DE-627 rakwb eng Li, Jun verfasserin aut Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection 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 The basic salp swarm algorithm (SSA) is a novel nature-inspired swarm intelligence optimization algorithm based on the foraging behavior of salp individuals in the deep sea. Since its development, the salp swarm algorithm has attracted widespread interest from scholars both at home and abroad for solving complex real-world practical problems. With continuous research, the SSA algorithm has revealed some shortcomings such as slow convergence speed and low accuracy. To enhance the optimization capability of the algorithm, in this paper, we propose an improved hybrid algorithm called TLSSA based on two phases of the teaching–learning-based optimization method: the teaching phase and the learner phase. In the teaching phase, students' ability is improved by updating the difference between the teacher and the class average level, which helps to improve the overall learning ability of the salp population, resulting in higher quality solutions. In the learning phase, by simulating the discussion, statement, and communication between students, the average level of the individual is improved, and the global search speed of the algorithm is accelerated. To verify the effectiveness and competitiveness of the proposed method, it is first tested on 30 IEEE CEC 2017 benchmark functions. The test results demonstrate that the proposed TLSSA method obtains better overall performance compared to 8 mainstream meta-heuristics and 8 advanced algorithms. After that, we applied the proposed method to solve two classical real-world engineering design problems and feature selection. Again, the experimental results show that our method has significant advantages over the traditional methods in solving these practical problems. The remarkable performance of our proposed improved TLSSA algorithm in solving theoretical and practical complex optimization problems also provides potential possibilities for applying more intelligent optimization algorithms to solve complex optimization problems in real-life situations in the future. Salp swarm algorithm (dpeaa)DE-He213 Teaching–learning-based optimization (dpeaa)DE-He213 Engineering design (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Ren, Hao aut Chen, Huiling aut Li, ChenYang aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 23 vom: 18. Aug., Seite 17887-17908 (DE-627)SPR006469531 nnns volume:27 year:2023 number:23 day:18 month:08 pages:17887-17908 https://dx.doi.org/10.1007/s00500-023-09070-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 23 18 08 17887-17908 |
spelling |
10.1007/s00500-023-09070-3 doi (DE-627)SPR053395875 (SPR)s00500-023-09070-3-e DE-627 ger DE-627 rakwb eng Li, Jun verfasserin aut Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection 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 The basic salp swarm algorithm (SSA) is a novel nature-inspired swarm intelligence optimization algorithm based on the foraging behavior of salp individuals in the deep sea. Since its development, the salp swarm algorithm has attracted widespread interest from scholars both at home and abroad for solving complex real-world practical problems. With continuous research, the SSA algorithm has revealed some shortcomings such as slow convergence speed and low accuracy. To enhance the optimization capability of the algorithm, in this paper, we propose an improved hybrid algorithm called TLSSA based on two phases of the teaching–learning-based optimization method: the teaching phase and the learner phase. In the teaching phase, students' ability is improved by updating the difference between the teacher and the class average level, which helps to improve the overall learning ability of the salp population, resulting in higher quality solutions. In the learning phase, by simulating the discussion, statement, and communication between students, the average level of the individual is improved, and the global search speed of the algorithm is accelerated. To verify the effectiveness and competitiveness of the proposed method, it is first tested on 30 IEEE CEC 2017 benchmark functions. The test results demonstrate that the proposed TLSSA method obtains better overall performance compared to 8 mainstream meta-heuristics and 8 advanced algorithms. After that, we applied the proposed method to solve two classical real-world engineering design problems and feature selection. Again, the experimental results show that our method has significant advantages over the traditional methods in solving these practical problems. The remarkable performance of our proposed improved TLSSA algorithm in solving theoretical and practical complex optimization problems also provides potential possibilities for applying more intelligent optimization algorithms to solve complex optimization problems in real-life situations in the future. Salp swarm algorithm (dpeaa)DE-He213 Teaching–learning-based optimization (dpeaa)DE-He213 Engineering design (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Ren, Hao aut Chen, Huiling aut Li, ChenYang aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 23 vom: 18. Aug., Seite 17887-17908 (DE-627)SPR006469531 nnns volume:27 year:2023 number:23 day:18 month:08 pages:17887-17908 https://dx.doi.org/10.1007/s00500-023-09070-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 23 18 08 17887-17908 |
allfields_unstemmed |
10.1007/s00500-023-09070-3 doi (DE-627)SPR053395875 (SPR)s00500-023-09070-3-e DE-627 ger DE-627 rakwb eng Li, Jun verfasserin aut Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection 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 The basic salp swarm algorithm (SSA) is a novel nature-inspired swarm intelligence optimization algorithm based on the foraging behavior of salp individuals in the deep sea. Since its development, the salp swarm algorithm has attracted widespread interest from scholars both at home and abroad for solving complex real-world practical problems. With continuous research, the SSA algorithm has revealed some shortcomings such as slow convergence speed and low accuracy. To enhance the optimization capability of the algorithm, in this paper, we propose an improved hybrid algorithm called TLSSA based on two phases of the teaching–learning-based optimization method: the teaching phase and the learner phase. In the teaching phase, students' ability is improved by updating the difference between the teacher and the class average level, which helps to improve the overall learning ability of the salp population, resulting in higher quality solutions. In the learning phase, by simulating the discussion, statement, and communication between students, the average level of the individual is improved, and the global search speed of the algorithm is accelerated. To verify the effectiveness and competitiveness of the proposed method, it is first tested on 30 IEEE CEC 2017 benchmark functions. The test results demonstrate that the proposed TLSSA method obtains better overall performance compared to 8 mainstream meta-heuristics and 8 advanced algorithms. After that, we applied the proposed method to solve two classical real-world engineering design problems and feature selection. Again, the experimental results show that our method has significant advantages over the traditional methods in solving these practical problems. The remarkable performance of our proposed improved TLSSA algorithm in solving theoretical and practical complex optimization problems also provides potential possibilities for applying more intelligent optimization algorithms to solve complex optimization problems in real-life situations in the future. Salp swarm algorithm (dpeaa)DE-He213 Teaching–learning-based optimization (dpeaa)DE-He213 Engineering design (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Ren, Hao aut Chen, Huiling aut Li, ChenYang aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 23 vom: 18. Aug., Seite 17887-17908 (DE-627)SPR006469531 nnns volume:27 year:2023 number:23 day:18 month:08 pages:17887-17908 https://dx.doi.org/10.1007/s00500-023-09070-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 23 18 08 17887-17908 |
allfieldsGer |
10.1007/s00500-023-09070-3 doi (DE-627)SPR053395875 (SPR)s00500-023-09070-3-e DE-627 ger DE-627 rakwb eng Li, Jun verfasserin aut Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection 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 The basic salp swarm algorithm (SSA) is a novel nature-inspired swarm intelligence optimization algorithm based on the foraging behavior of salp individuals in the deep sea. Since its development, the salp swarm algorithm has attracted widespread interest from scholars both at home and abroad for solving complex real-world practical problems. With continuous research, the SSA algorithm has revealed some shortcomings such as slow convergence speed and low accuracy. To enhance the optimization capability of the algorithm, in this paper, we propose an improved hybrid algorithm called TLSSA based on two phases of the teaching–learning-based optimization method: the teaching phase and the learner phase. In the teaching phase, students' ability is improved by updating the difference between the teacher and the class average level, which helps to improve the overall learning ability of the salp population, resulting in higher quality solutions. In the learning phase, by simulating the discussion, statement, and communication between students, the average level of the individual is improved, and the global search speed of the algorithm is accelerated. To verify the effectiveness and competitiveness of the proposed method, it is first tested on 30 IEEE CEC 2017 benchmark functions. The test results demonstrate that the proposed TLSSA method obtains better overall performance compared to 8 mainstream meta-heuristics and 8 advanced algorithms. After that, we applied the proposed method to solve two classical real-world engineering design problems and feature selection. Again, the experimental results show that our method has significant advantages over the traditional methods in solving these practical problems. The remarkable performance of our proposed improved TLSSA algorithm in solving theoretical and practical complex optimization problems also provides potential possibilities for applying more intelligent optimization algorithms to solve complex optimization problems in real-life situations in the future. Salp swarm algorithm (dpeaa)DE-He213 Teaching–learning-based optimization (dpeaa)DE-He213 Engineering design (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Ren, Hao aut Chen, Huiling aut Li, ChenYang aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 23 vom: 18. Aug., Seite 17887-17908 (DE-627)SPR006469531 nnns volume:27 year:2023 number:23 day:18 month:08 pages:17887-17908 https://dx.doi.org/10.1007/s00500-023-09070-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 23 18 08 17887-17908 |
allfieldsSound |
10.1007/s00500-023-09070-3 doi (DE-627)SPR053395875 (SPR)s00500-023-09070-3-e DE-627 ger DE-627 rakwb eng Li, Jun verfasserin aut Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection 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 The basic salp swarm algorithm (SSA) is a novel nature-inspired swarm intelligence optimization algorithm based on the foraging behavior of salp individuals in the deep sea. Since its development, the salp swarm algorithm has attracted widespread interest from scholars both at home and abroad for solving complex real-world practical problems. With continuous research, the SSA algorithm has revealed some shortcomings such as slow convergence speed and low accuracy. To enhance the optimization capability of the algorithm, in this paper, we propose an improved hybrid algorithm called TLSSA based on two phases of the teaching–learning-based optimization method: the teaching phase and the learner phase. In the teaching phase, students' ability is improved by updating the difference between the teacher and the class average level, which helps to improve the overall learning ability of the salp population, resulting in higher quality solutions. In the learning phase, by simulating the discussion, statement, and communication between students, the average level of the individual is improved, and the global search speed of the algorithm is accelerated. To verify the effectiveness and competitiveness of the proposed method, it is first tested on 30 IEEE CEC 2017 benchmark functions. The test results demonstrate that the proposed TLSSA method obtains better overall performance compared to 8 mainstream meta-heuristics and 8 advanced algorithms. After that, we applied the proposed method to solve two classical real-world engineering design problems and feature selection. Again, the experimental results show that our method has significant advantages over the traditional methods in solving these practical problems. The remarkable performance of our proposed improved TLSSA algorithm in solving theoretical and practical complex optimization problems also provides potential possibilities for applying more intelligent optimization algorithms to solve complex optimization problems in real-life situations in the future. Salp swarm algorithm (dpeaa)DE-He213 Teaching–learning-based optimization (dpeaa)DE-He213 Engineering design (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 Ren, Hao aut Chen, Huiling aut Li, ChenYang aut Enthalten in Soft Computing Springer-Verlag, 2003 27(2023), 23 vom: 18. Aug., Seite 17887-17908 (DE-627)SPR006469531 nnns volume:27 year:2023 number:23 day:18 month:08 pages:17887-17908 https://dx.doi.org/10.1007/s00500-023-09070-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 27 2023 23 18 08 17887-17908 |
language |
English |
source |
Enthalten in Soft Computing 27(2023), 23 vom: 18. Aug., Seite 17887-17908 volume:27 year:2023 number:23 day:18 month:08 pages:17887-17908 |
sourceStr |
Enthalten in Soft Computing 27(2023), 23 vom: 18. Aug., Seite 17887-17908 volume:27 year:2023 number:23 day:18 month:08 pages:17887-17908 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Salp swarm algorithm Teaching–learning-based optimization Engineering design Feature selection |
isfreeaccess_bool |
false |
container_title |
Soft Computing |
authorswithroles_txt_mv |
Li, Jun @@aut@@ Ren, Hao @@aut@@ Chen, Huiling @@aut@@ Li, ChenYang @@aut@@ |
publishDateDaySort_date |
2023-08-18T00:00:00Z |
hierarchy_top_id |
SPR006469531 |
id |
SPR053395875 |
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">SPR053395875</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231014064635.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231014s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-023-09070-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053395875</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-023-09070-3-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">Li, Jun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection</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 The basic salp swarm algorithm (SSA) is a novel nature-inspired swarm intelligence optimization algorithm based on the foraging behavior of salp individuals in the deep sea. Since its development, the salp swarm algorithm has attracted widespread interest from scholars both at home and abroad for solving complex real-world practical problems. With continuous research, the SSA algorithm has revealed some shortcomings such as slow convergence speed and low accuracy. To enhance the optimization capability of the algorithm, in this paper, we propose an improved hybrid algorithm called TLSSA based on two phases of the teaching–learning-based optimization method: the teaching phase and the learner phase. In the teaching phase, students' ability is improved by updating the difference between the teacher and the class average level, which helps to improve the overall learning ability of the salp population, resulting in higher quality solutions. In the learning phase, by simulating the discussion, statement, and communication between students, the average level of the individual is improved, and the global search speed of the algorithm is accelerated. To verify the effectiveness and competitiveness of the proposed method, it is first tested on 30 IEEE CEC 2017 benchmark functions. The test results demonstrate that the proposed TLSSA method obtains better overall performance compared to 8 mainstream meta-heuristics and 8 advanced algorithms. After that, we applied the proposed method to solve two classical real-world engineering design problems and feature selection. Again, the experimental results show that our method has significant advantages over the traditional methods in solving these practical problems. The remarkable performance of our proposed improved TLSSA algorithm in solving theoretical and practical complex optimization problems also provides potential possibilities for applying more intelligent optimization algorithms to solve complex optimization problems in real-life situations in the future.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Salp swarm algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Teaching–learning-based optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Engineering design</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature selection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ren, Hao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Huiling</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, ChenYang</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), 23 vom: 18. Aug., Seite 17887-17908</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:23</subfield><subfield code="g">day:18</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:17887-17908</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-023-09070-3</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">23</subfield><subfield code="b">18</subfield><subfield code="c">08</subfield><subfield code="h">17887-17908</subfield></datafield></record></collection>
|
author |
Li, Jun |
spellingShingle |
Li, Jun misc Salp swarm algorithm misc Teaching–learning-based optimization misc Engineering design misc Feature selection Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection |
authorStr |
Li, Jun |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)SPR006469531 |
format |
electronic Article |
delete_txt_mv |
keep |
author_role |
aut aut aut aut |
collection |
springer |
remote_str |
true |
illustrated |
Not Illustrated |
topic_title |
Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection Salp swarm algorithm (dpeaa)DE-He213 Teaching–learning-based optimization (dpeaa)DE-He213 Engineering design (dpeaa)DE-He213 Feature selection (dpeaa)DE-He213 |
topic |
misc Salp swarm algorithm misc Teaching–learning-based optimization misc Engineering design misc Feature selection |
topic_unstemmed |
misc Salp swarm algorithm misc Teaching–learning-based optimization misc Engineering design misc Feature selection |
topic_browse |
misc Salp swarm algorithm misc Teaching–learning-based optimization misc Engineering design misc Feature selection |
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 |
Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection |
ctrlnum |
(DE-627)SPR053395875 (SPR)s00500-023-09070-3-e |
title_full |
Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection |
author_sort |
Li, Jun |
journal |
Soft Computing |
journalStr |
Soft Computing |
lang_code |
eng |
isOA_bool |
false |
recordtype |
marc |
publishDateSort |
2023 |
contenttype_str_mv |
txt |
container_start_page |
17887 |
author_browse |
Li, Jun Ren, Hao Chen, Huiling Li, ChenYang |
container_volume |
27 |
format_se |
Elektronische Aufsätze |
author-letter |
Li, Jun |
doi_str_mv |
10.1007/s00500-023-09070-3 |
title_sort |
teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection |
title_auth |
Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection |
abstract |
Abstract The basic salp swarm algorithm (SSA) is a novel nature-inspired swarm intelligence optimization algorithm based on the foraging behavior of salp individuals in the deep sea. Since its development, the salp swarm algorithm has attracted widespread interest from scholars both at home and abroad for solving complex real-world practical problems. With continuous research, the SSA algorithm has revealed some shortcomings such as slow convergence speed and low accuracy. To enhance the optimization capability of the algorithm, in this paper, we propose an improved hybrid algorithm called TLSSA based on two phases of the teaching–learning-based optimization method: the teaching phase and the learner phase. In the teaching phase, students' ability is improved by updating the difference between the teacher and the class average level, which helps to improve the overall learning ability of the salp population, resulting in higher quality solutions. In the learning phase, by simulating the discussion, statement, and communication between students, the average level of the individual is improved, and the global search speed of the algorithm is accelerated. To verify the effectiveness and competitiveness of the proposed method, it is first tested on 30 IEEE CEC 2017 benchmark functions. The test results demonstrate that the proposed TLSSA method obtains better overall performance compared to 8 mainstream meta-heuristics and 8 advanced algorithms. After that, we applied the proposed method to solve two classical real-world engineering design problems and feature selection. Again, the experimental results show that our method has significant advantages over the traditional methods in solving these practical problems. The remarkable performance of our proposed improved TLSSA algorithm in solving theoretical and practical complex optimization problems also provides potential possibilities for applying more intelligent optimization algorithms to solve complex optimization problems in real-life situations in the future. © 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 The basic salp swarm algorithm (SSA) is a novel nature-inspired swarm intelligence optimization algorithm based on the foraging behavior of salp individuals in the deep sea. Since its development, the salp swarm algorithm has attracted widespread interest from scholars both at home and abroad for solving complex real-world practical problems. With continuous research, the SSA algorithm has revealed some shortcomings such as slow convergence speed and low accuracy. To enhance the optimization capability of the algorithm, in this paper, we propose an improved hybrid algorithm called TLSSA based on two phases of the teaching–learning-based optimization method: the teaching phase and the learner phase. In the teaching phase, students' ability is improved by updating the difference between the teacher and the class average level, which helps to improve the overall learning ability of the salp population, resulting in higher quality solutions. In the learning phase, by simulating the discussion, statement, and communication between students, the average level of the individual is improved, and the global search speed of the algorithm is accelerated. To verify the effectiveness and competitiveness of the proposed method, it is first tested on 30 IEEE CEC 2017 benchmark functions. The test results demonstrate that the proposed TLSSA method obtains better overall performance compared to 8 mainstream meta-heuristics and 8 advanced algorithms. After that, we applied the proposed method to solve two classical real-world engineering design problems and feature selection. Again, the experimental results show that our method has significant advantages over the traditional methods in solving these practical problems. The remarkable performance of our proposed improved TLSSA algorithm in solving theoretical and practical complex optimization problems also provides potential possibilities for applying more intelligent optimization algorithms to solve complex optimization problems in real-life situations in the future. © 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 The basic salp swarm algorithm (SSA) is a novel nature-inspired swarm intelligence optimization algorithm based on the foraging behavior of salp individuals in the deep sea. Since its development, the salp swarm algorithm has attracted widespread interest from scholars both at home and abroad for solving complex real-world practical problems. With continuous research, the SSA algorithm has revealed some shortcomings such as slow convergence speed and low accuracy. To enhance the optimization capability of the algorithm, in this paper, we propose an improved hybrid algorithm called TLSSA based on two phases of the teaching–learning-based optimization method: the teaching phase and the learner phase. In the teaching phase, students' ability is improved by updating the difference between the teacher and the class average level, which helps to improve the overall learning ability of the salp population, resulting in higher quality solutions. In the learning phase, by simulating the discussion, statement, and communication between students, the average level of the individual is improved, and the global search speed of the algorithm is accelerated. To verify the effectiveness and competitiveness of the proposed method, it is first tested on 30 IEEE CEC 2017 benchmark functions. The test results demonstrate that the proposed TLSSA method obtains better overall performance compared to 8 mainstream meta-heuristics and 8 advanced algorithms. After that, we applied the proposed method to solve two classical real-world engineering design problems and feature selection. Again, the experimental results show that our method has significant advantages over the traditional methods in solving these practical problems. The remarkable performance of our proposed improved TLSSA algorithm in solving theoretical and practical complex optimization problems also provides potential possibilities for applying more intelligent optimization algorithms to solve complex optimization problems in real-life situations in the future. © 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 |
23 |
title_short |
Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection |
url |
https://dx.doi.org/10.1007/s00500-023-09070-3 |
remote_bool |
true |
author2 |
Ren, Hao Chen, Huiling Li, ChenYang |
author2Str |
Ren, Hao Chen, Huiling Li, ChenYang |
ppnlink |
SPR006469531 |
mediatype_str_mv |
c |
isOA_txt |
false |
hochschulschrift_bool |
false |
doi_str |
10.1007/s00500-023-09070-3 |
up_date |
2024-07-03T19:12:54.211Z |
_version_ |
1803586344546467840 |
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">SPR053395875</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231014064635.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231014s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-023-09070-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053395875</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-023-09070-3-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">Li, Jun</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Teaching–learning guided salp swarm algorithm for global optimization tasks and feature selection</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 The basic salp swarm algorithm (SSA) is a novel nature-inspired swarm intelligence optimization algorithm based on the foraging behavior of salp individuals in the deep sea. Since its development, the salp swarm algorithm has attracted widespread interest from scholars both at home and abroad for solving complex real-world practical problems. With continuous research, the SSA algorithm has revealed some shortcomings such as slow convergence speed and low accuracy. To enhance the optimization capability of the algorithm, in this paper, we propose an improved hybrid algorithm called TLSSA based on two phases of the teaching–learning-based optimization method: the teaching phase and the learner phase. In the teaching phase, students' ability is improved by updating the difference between the teacher and the class average level, which helps to improve the overall learning ability of the salp population, resulting in higher quality solutions. In the learning phase, by simulating the discussion, statement, and communication between students, the average level of the individual is improved, and the global search speed of the algorithm is accelerated. To verify the effectiveness and competitiveness of the proposed method, it is first tested on 30 IEEE CEC 2017 benchmark functions. The test results demonstrate that the proposed TLSSA method obtains better overall performance compared to 8 mainstream meta-heuristics and 8 advanced algorithms. After that, we applied the proposed method to solve two classical real-world engineering design problems and feature selection. Again, the experimental results show that our method has significant advantages over the traditional methods in solving these practical problems. The remarkable performance of our proposed improved TLSSA algorithm in solving theoretical and practical complex optimization problems also provides potential possibilities for applying more intelligent optimization algorithms to solve complex optimization problems in real-life situations in the future.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Salp swarm algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Teaching–learning-based optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Engineering design</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Feature selection</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ren, Hao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Huiling</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, ChenYang</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), 23 vom: 18. Aug., Seite 17887-17908</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:23</subfield><subfield code="g">day:18</subfield><subfield code="g">month:08</subfield><subfield code="g">pages:17887-17908</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-023-09070-3</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">23</subfield><subfield code="b">18</subfield><subfield code="c">08</subfield><subfield code="h">17887-17908</subfield></datafield></record></collection>
|
score |
7.399928 |