Experimental analysis of design elements of scalarizing function-based multiobjective evolutionary algorithms
Abstract In this paper, we systematically study the influence of the main design elements of scalarizing function-based multiobjective evolutionary algorithms (MOEAs) on the performance of these algorithms. Such algorithms proved to be very successful in multiple computational experiments and practi...
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| Autor*in: |
Aghabeig, Mansoureh [verfasserIn] Jaszkiewicz, Andrzej [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 23(2018), 21 vom: 28. Nov., Seite 10769-10780 |
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| Übergeordnetes Werk: |
volume:23 ; year:2018 ; number:21 ; day:28 ; month:11 ; pages:10769-10780 |
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10.1007/s00500-018-3631-x |
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| 520 | |a Abstract In this paper, we systematically study the influence of the main design elements of scalarizing function-based multiobjective evolutionary algorithms (MOEAs) on the performance of these algorithms. Such algorithms proved to be very successful in multiple computational experiments and practical applications. Well-known examples of this class of MOEAs are Jaszkiewicz’s multiobjecitve genetic local search and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The two algorithms share the same common structure and differ in two aspects, i.e., the selection of parents for recombination and the selection of weight vectors of scalarizing functions. Using three different multiobjective combinatorial optimization problems, i.e., the multiobjective symmetric traveling salesperson problem, the traveling salesperson problem with profits, and the multiobjective set covering problem, we show that the design element with the highest influence on the performance is the choice of a mechanism for parents selection, while the selection of weight vectors, either random or evenly distributed, has practically negligible influence if the number of evenly distributed weight vectors is sufficiently large. | ||
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10.1007/s00500-018-3631-x doi (DE-627)SPR006508138 (SPR)s00500-018-3631-x-e DE-627 ger DE-627 rakwb eng Aghabeig, Mansoureh verfasserin aut Experimental analysis of design elements of scalarizing function-based multiobjective evolutionary algorithms 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we systematically study the influence of the main design elements of scalarizing function-based multiobjective evolutionary algorithms (MOEAs) on the performance of these algorithms. Such algorithms proved to be very successful in multiple computational experiments and practical applications. Well-known examples of this class of MOEAs are Jaszkiewicz’s multiobjecitve genetic local search and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The two algorithms share the same common structure and differ in two aspects, i.e., the selection of parents for recombination and the selection of weight vectors of scalarizing functions. Using three different multiobjective combinatorial optimization problems, i.e., the multiobjective symmetric traveling salesperson problem, the traveling salesperson problem with profits, and the multiobjective set covering problem, we show that the design element with the highest influence on the performance is the choice of a mechanism for parents selection, while the selection of weight vectors, either random or evenly distributed, has practically negligible influence if the number of evenly distributed weight vectors is sufficiently large. Metaheuristics (dpeaa)DE-He213 Multiobjective evolutionary algorithms (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Traveling salesperson problem (dpeaa)DE-He213 Set covering problem (dpeaa)DE-He213 Jaszkiewicz, Andrzej verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2018), 21 vom: 28. Nov., Seite 10769-10780 (DE-627)SPR006469531 nnns volume:23 year:2018 number:21 day:28 month:11 pages:10769-10780 https://dx.doi.org/10.1007/s00500-018-3631-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 21 28 11 10769-10780 |
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10.1007/s00500-018-3631-x doi (DE-627)SPR006508138 (SPR)s00500-018-3631-x-e DE-627 ger DE-627 rakwb eng Aghabeig, Mansoureh verfasserin aut Experimental analysis of design elements of scalarizing function-based multiobjective evolutionary algorithms 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we systematically study the influence of the main design elements of scalarizing function-based multiobjective evolutionary algorithms (MOEAs) on the performance of these algorithms. Such algorithms proved to be very successful in multiple computational experiments and practical applications. Well-known examples of this class of MOEAs are Jaszkiewicz’s multiobjecitve genetic local search and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The two algorithms share the same common structure and differ in two aspects, i.e., the selection of parents for recombination and the selection of weight vectors of scalarizing functions. Using three different multiobjective combinatorial optimization problems, i.e., the multiobjective symmetric traveling salesperson problem, the traveling salesperson problem with profits, and the multiobjective set covering problem, we show that the design element with the highest influence on the performance is the choice of a mechanism for parents selection, while the selection of weight vectors, either random or evenly distributed, has practically negligible influence if the number of evenly distributed weight vectors is sufficiently large. Metaheuristics (dpeaa)DE-He213 Multiobjective evolutionary algorithms (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Traveling salesperson problem (dpeaa)DE-He213 Set covering problem (dpeaa)DE-He213 Jaszkiewicz, Andrzej verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2018), 21 vom: 28. Nov., Seite 10769-10780 (DE-627)SPR006469531 nnns volume:23 year:2018 number:21 day:28 month:11 pages:10769-10780 https://dx.doi.org/10.1007/s00500-018-3631-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 21 28 11 10769-10780 |
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10.1007/s00500-018-3631-x doi (DE-627)SPR006508138 (SPR)s00500-018-3631-x-e DE-627 ger DE-627 rakwb eng Aghabeig, Mansoureh verfasserin aut Experimental analysis of design elements of scalarizing function-based multiobjective evolutionary algorithms 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we systematically study the influence of the main design elements of scalarizing function-based multiobjective evolutionary algorithms (MOEAs) on the performance of these algorithms. Such algorithms proved to be very successful in multiple computational experiments and practical applications. Well-known examples of this class of MOEAs are Jaszkiewicz’s multiobjecitve genetic local search and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The two algorithms share the same common structure and differ in two aspects, i.e., the selection of parents for recombination and the selection of weight vectors of scalarizing functions. Using three different multiobjective combinatorial optimization problems, i.e., the multiobjective symmetric traveling salesperson problem, the traveling salesperson problem with profits, and the multiobjective set covering problem, we show that the design element with the highest influence on the performance is the choice of a mechanism for parents selection, while the selection of weight vectors, either random or evenly distributed, has practically negligible influence if the number of evenly distributed weight vectors is sufficiently large. Metaheuristics (dpeaa)DE-He213 Multiobjective evolutionary algorithms (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Traveling salesperson problem (dpeaa)DE-He213 Set covering problem (dpeaa)DE-He213 Jaszkiewicz, Andrzej verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2018), 21 vom: 28. Nov., Seite 10769-10780 (DE-627)SPR006469531 nnns volume:23 year:2018 number:21 day:28 month:11 pages:10769-10780 https://dx.doi.org/10.1007/s00500-018-3631-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 21 28 11 10769-10780 |
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10.1007/s00500-018-3631-x doi (DE-627)SPR006508138 (SPR)s00500-018-3631-x-e DE-627 ger DE-627 rakwb eng Aghabeig, Mansoureh verfasserin aut Experimental analysis of design elements of scalarizing function-based multiobjective evolutionary algorithms 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we systematically study the influence of the main design elements of scalarizing function-based multiobjective evolutionary algorithms (MOEAs) on the performance of these algorithms. Such algorithms proved to be very successful in multiple computational experiments and practical applications. Well-known examples of this class of MOEAs are Jaszkiewicz’s multiobjecitve genetic local search and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The two algorithms share the same common structure and differ in two aspects, i.e., the selection of parents for recombination and the selection of weight vectors of scalarizing functions. Using three different multiobjective combinatorial optimization problems, i.e., the multiobjective symmetric traveling salesperson problem, the traveling salesperson problem with profits, and the multiobjective set covering problem, we show that the design element with the highest influence on the performance is the choice of a mechanism for parents selection, while the selection of weight vectors, either random or evenly distributed, has practically negligible influence if the number of evenly distributed weight vectors is sufficiently large. Metaheuristics (dpeaa)DE-He213 Multiobjective evolutionary algorithms (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Traveling salesperson problem (dpeaa)DE-He213 Set covering problem (dpeaa)DE-He213 Jaszkiewicz, Andrzej verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2018), 21 vom: 28. Nov., Seite 10769-10780 (DE-627)SPR006469531 nnns volume:23 year:2018 number:21 day:28 month:11 pages:10769-10780 https://dx.doi.org/10.1007/s00500-018-3631-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 21 28 11 10769-10780 |
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10.1007/s00500-018-3631-x doi (DE-627)SPR006508138 (SPR)s00500-018-3631-x-e DE-627 ger DE-627 rakwb eng Aghabeig, Mansoureh verfasserin aut Experimental analysis of design elements of scalarizing function-based multiobjective evolutionary algorithms 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract In this paper, we systematically study the influence of the main design elements of scalarizing function-based multiobjective evolutionary algorithms (MOEAs) on the performance of these algorithms. Such algorithms proved to be very successful in multiple computational experiments and practical applications. Well-known examples of this class of MOEAs are Jaszkiewicz’s multiobjecitve genetic local search and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The two algorithms share the same common structure and differ in two aspects, i.e., the selection of parents for recombination and the selection of weight vectors of scalarizing functions. Using three different multiobjective combinatorial optimization problems, i.e., the multiobjective symmetric traveling salesperson problem, the traveling salesperson problem with profits, and the multiobjective set covering problem, we show that the design element with the highest influence on the performance is the choice of a mechanism for parents selection, while the selection of weight vectors, either random or evenly distributed, has practically negligible influence if the number of evenly distributed weight vectors is sufficiently large. Metaheuristics (dpeaa)DE-He213 Multiobjective evolutionary algorithms (dpeaa)DE-He213 Combinatorial optimization (dpeaa)DE-He213 Traveling salesperson problem (dpeaa)DE-He213 Set covering problem (dpeaa)DE-He213 Jaszkiewicz, Andrzej verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2018), 21 vom: 28. Nov., Seite 10769-10780 (DE-627)SPR006469531 nnns volume:23 year:2018 number:21 day:28 month:11 pages:10769-10780 https://dx.doi.org/10.1007/s00500-018-3631-x kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2018 21 28 11 10769-10780 |
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Abstract In this paper, we systematically study the influence of the main design elements of scalarizing function-based multiobjective evolutionary algorithms (MOEAs) on the performance of these algorithms. Such algorithms proved to be very successful in multiple computational experiments and practical applications. Well-known examples of this class of MOEAs are Jaszkiewicz’s multiobjecitve genetic local search and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The two algorithms share the same common structure and differ in two aspects, i.e., the selection of parents for recombination and the selection of weight vectors of scalarizing functions. Using three different multiobjective combinatorial optimization problems, i.e., the multiobjective symmetric traveling salesperson problem, the traveling salesperson problem with profits, and the multiobjective set covering problem, we show that the design element with the highest influence on the performance is the choice of a mechanism for parents selection, while the selection of weight vectors, either random or evenly distributed, has practically negligible influence if the number of evenly distributed weight vectors is sufficiently large. |
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Abstract In this paper, we systematically study the influence of the main design elements of scalarizing function-based multiobjective evolutionary algorithms (MOEAs) on the performance of these algorithms. Such algorithms proved to be very successful in multiple computational experiments and practical applications. Well-known examples of this class of MOEAs are Jaszkiewicz’s multiobjecitve genetic local search and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The two algorithms share the same common structure and differ in two aspects, i.e., the selection of parents for recombination and the selection of weight vectors of scalarizing functions. Using three different multiobjective combinatorial optimization problems, i.e., the multiobjective symmetric traveling salesperson problem, the traveling salesperson problem with profits, and the multiobjective set covering problem, we show that the design element with the highest influence on the performance is the choice of a mechanism for parents selection, while the selection of weight vectors, either random or evenly distributed, has practically negligible influence if the number of evenly distributed weight vectors is sufficiently large. |
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Abstract In this paper, we systematically study the influence of the main design elements of scalarizing function-based multiobjective evolutionary algorithms (MOEAs) on the performance of these algorithms. Such algorithms proved to be very successful in multiple computational experiments and practical applications. Well-known examples of this class of MOEAs are Jaszkiewicz’s multiobjecitve genetic local search and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The two algorithms share the same common structure and differ in two aspects, i.e., the selection of parents for recombination and the selection of weight vectors of scalarizing functions. Using three different multiobjective combinatorial optimization problems, i.e., the multiobjective symmetric traveling salesperson problem, the traveling salesperson problem with profits, and the multiobjective set covering problem, we show that the design element with the highest influence on the performance is the choice of a mechanism for parents selection, while the selection of weight vectors, either random or evenly distributed, has practically negligible influence if the number of evenly distributed weight vectors is sufficiently large. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR006508138</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002914.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2018 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-018-3631-x</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006508138</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-018-3631-x-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">Aghabeig, Mansoureh</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Experimental analysis of design elements of scalarizing function-based multiobjective evolutionary algorithms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2018</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="520" ind1=" " ind2=" "><subfield code="a">Abstract In this paper, we systematically study the influence of the main design elements of scalarizing function-based multiobjective evolutionary algorithms (MOEAs) on the performance of these algorithms. Such algorithms proved to be very successful in multiple computational experiments and practical applications. Well-known examples of this class of MOEAs are Jaszkiewicz’s multiobjecitve genetic local search and multiobjective evolutionary algorithm based on decomposition (MOEA/D). The two algorithms share the same common structure and differ in two aspects, i.e., the selection of parents for recombination and the selection of weight vectors of scalarizing functions. Using three different multiobjective combinatorial optimization problems, i.e., the multiobjective symmetric traveling salesperson problem, the traveling salesperson problem with profits, and the multiobjective set covering problem, we show that the design element with the highest influence on the performance is the choice of a mechanism for parents selection, while the selection of weight vectors, either random or evenly distributed, has practically negligible influence if the number of evenly distributed weight vectors is sufficiently large.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Metaheuristics</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multiobjective evolutionary algorithms</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Combinatorial optimization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Traveling salesperson problem</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Set covering problem</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jaszkiewicz, Andrzej</subfield><subfield code="e">verfasserin</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">23(2018), 21 vom: 28. Nov., Seite 10769-10780</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:23</subfield><subfield code="g">year:2018</subfield><subfield code="g">number:21</subfield><subfield code="g">day:28</subfield><subfield code="g">month:11</subfield><subfield code="g">pages:10769-10780</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s00500-018-3631-x</subfield><subfield code="z">kostenfrei</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">23</subfield><subfield code="j">2018</subfield><subfield code="e">21</subfield><subfield code="b">28</subfield><subfield code="c">11</subfield><subfield code="h">10769-10780</subfield></datafield></record></collection>
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