Publicação
Scalable and customizable benchmark problems for many-objective optimization
| Resumo: | Solving many-objective problems (MaOPs) is still a significant challenge in the multi-objective optimization (MOO) field. One way to measure algorithm performance is through the use of benchmark functions (also called test functions or test suites), which are artificial problems with a well-defined mathematical formulation, known solutions and a variety of features and difficulties. In this paper we propose a parameterized generator of scalable and customizable benchmark problems for MaOPs. It is able to generate problems that reproduce features present in other benchmarks and also problems with some new features. We propose here the concept of generative benchmarking, in which one can generate an infinite number of MOO problems, by varying parameters that control specific features that the problem should have: scalability in the number of variables and objectives, bias, deceptiveness, multimodality, robust and non-robust solutions, shape of the Pareto front, and constraints. The proposed Generalized Position-Distance (GPD) tunable benchmark generator uses the position-distance paradigm, a basic approach to building test functions, used in other benchmarks such as Deb, Thiele, Laumanns and Zitzler (DTLZ), Walking Fish Group (WFG) and others. It includes scalable problems in any number of variables and objectives and it presents Pareto fronts with different characteristics. The resulting functions are easy to understand and visualize, easy to implement, fast to compute and their Pareto optimal solutions are known. |
|---|---|
| Autores principais: | Meneghini, Ivan Reinaldo |
| Outros Autores: | Alves, Marcos Antonio; Gaspar-Cunha, A.; Guimarães, Frederico Gadelha |
| Assunto: | Benchmark functions Scalable test functions Many-objective optimization Evolutionary algorithms |
| Ano: | 2020 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1866270340724817920 |
|---|---|
| author | Meneghini, Ivan Reinaldo |
| author2 | Alves, Marcos Antonio Gaspar-Cunha, A. Guimarães, Frederico Gadelha |
| author2_role | author author author |
| author_facet | Meneghini, Ivan Reinaldo Alves, Marcos Antonio Gaspar-Cunha, A. Guimarães, Frederico Gadelha |
| author_role | author |
| contributor_name_str_mv | Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Meneghini, Ivan Reinaldo\"},{\"Person.name\":\"Alves, Marcos Antonio\"},{\"Person.name\":\"Gaspar-Cunha, A.\"},{\"Person.name\":\"Guimarães, Frederico Gadelha\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Meneghini, Ivan Reinaldo Alves, Marcos Antonio Gaspar-Cunha, A. Guimarães, Frederico Gadelha |
| datacite.date.Accepted.fl_str_mv | 2020-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2020-12-21T10:33:41Z |
| datacite.date.embargoed.fl_str_mv | 2020-12-21T10:33:41Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Benchmark functions Scalable test functions Many-objective optimization Evolutionary algorithms |
| datacite.titles.title.fl_str_mv | Scalable and customizable benchmark problems for many-objective optimization |
| dc.contributor.none.fl_str_mv | Universidade do Minho |
| dc.creator.none.fl_str_mv | Meneghini, Ivan Reinaldo Alves, Marcos Antonio Gaspar-Cunha, A. Guimarães, Frederico Gadelha |
| dc.date.Accepted.fl_str_mv | 2020-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2020-12-21T10:33:41Z |
| dc.date.embargoed.fl_str_mv | 2020-12-21T10:33:41Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/68633 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Elsevier |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Benchmark functions Scalable test functions Many-objective optimization Evolutionary algorithms |
| dc.title.fl_str_mv | Scalable and customizable benchmark problems for many-objective optimization |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Solving many-objective problems (MaOPs) is still a significant challenge in the multi-objective optimization (MOO) field. One way to measure algorithm performance is through the use of benchmark functions (also called test functions or test suites), which are artificial problems with a well-defined mathematical formulation, known solutions and a variety of features and difficulties. In this paper we propose a parameterized generator of scalable and customizable benchmark problems for MaOPs. It is able to generate problems that reproduce features present in other benchmarks and also problems with some new features. We propose here the concept of generative benchmarking, in which one can generate an infinite number of MOO problems, by varying parameters that control specific features that the problem should have: scalability in the number of variables and objectives, bias, deceptiveness, multimodality, robust and non-robust solutions, shape of the Pareto front, and constraints. The proposed Generalized Position-Distance (GPD) tunable benchmark generator uses the position-distance paradigm, a basic approach to building test functions, used in other benchmarks such as Deb, Thiele, Laumanns and Zitzler (DTLZ), Walking Fish Group (WFG) and others. It includes scalable problems in any number of variables and objectives and it presents Pareto fronts with different characteristics. The resulting functions are easy to understand and visualize, easy to implement, fast to compute and their Pareto optimal solutions are known. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| fulltext.url.fl_str_mv | https://prod-dspace.uminho.pt/bitstreams/99d5ac14-7d30-4120-976a-d3aaa7da0b5c/download |
| id | rum_dd72fa6fbc9da0ac5847faee2a99a3a5 |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/68633 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/68633 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Meneghini, Ivan Reinaldo Alves, Marcos Antonio Gaspar-Cunha, A. Guimarães, Frederico Gadelha |
| publishDate | 2020 |
| publisher.none.fl_str_mv | Elsevier |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engElsevierporSolving many-objective problems (MaOPs) is still a significant challenge in the multi-objective optimization (MOO) field. One way to measure algorithm performance is through the use of benchmark functions (also called test functions or test suites), which are artificial problems with a well-defined mathematical formulation, known solutions and a variety of features and difficulties. In this paper we propose a parameterized generator of scalable and customizable benchmark problems for MaOPs. It is able to generate problems that reproduce features present in other benchmarks and also problems with some new features. We propose here the concept of generative benchmarking, in which one can generate an infinite number of MOO problems, by varying parameters that control specific features that the problem should have: scalability in the number of variables and objectives, bias, deceptiveness, multimodality, robust and non-robust solutions, shape of the Pareto front, and constraints. The proposed Generalized Position-Distance (GPD) tunable benchmark generator uses the position-distance paradigm, a basic approach to building test functions, used in other benchmarks such as Deb, Thiele, Laumanns and Zitzler (DTLZ), Walking Fish Group (WFG) and others. It includes scalable problems in any number of variables and objectives and it presents Pareto fronts with different characteristics. The resulting functions are easy to understand and visualize, easy to implement, fast to compute and their Pareto optimal solutions are known.application/pdfporScalable and customizable benchmark problems for many-objective optimizationMeneghini, Ivan ReinaldoAlves, Marcos AntonioGaspar-Cunha, A.Guimarães, Frederico GadelhaHostingInstitutionOrganizationalUniversidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptISSNIsPartOf1568-4946DOIIsPartOf10.1016/j.asoc.2020.1061392020-12-21T10:33:41Z20202020-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/68633http://purl.org/coar/access_right/c_abf2open accessBenchmark functionsScalable test functionsMany-objective optimizationEvolutionary algorithms3084332 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://prod-dspace.uminho.pt/bitstreams/99d5ac14-7d30-4120-976a-d3aaa7da0b5c/download |
| spellingShingle | Scalable and customizable benchmark problems for many-objective optimization Meneghini, Ivan Reinaldo Benchmark functions Scalable test functions Many-objective optimization Evolutionary algorithms |
| status | SINGLETON |
| subject.fl_str_mv | Benchmark functions Scalable test functions Many-objective optimization Evolutionary algorithms |
| title | Scalable and customizable benchmark problems for many-objective optimization |
| title_full | Scalable and customizable benchmark problems for many-objective optimization |
| title_fullStr | Scalable and customizable benchmark problems for many-objective optimization |
| title_full_unstemmed | Scalable and customizable benchmark problems for many-objective optimization |
| title_short | Scalable and customizable benchmark problems for many-objective optimization |
| title_sort | Scalable and customizable benchmark problems for many-objective optimization |
| topic | Benchmark functions Scalable test functions Many-objective optimization Evolutionary algorithms |
| topic_facet | Benchmark functions Scalable test functions Many-objective optimization Evolutionary algorithms |
| url | https://hdl.handle.net/1822/68633 |
| visible | 1 |