{"corpus_id":260397241,"paper_sha":"f155671c6bec0780aab09a919eb8242b61493b58","doi":"10.1109/TEVC.2023.3300181","arxiv_id":null,"pmid":null,"pmcid":null,"mag_id":null,"dblp_id":"journals/tec/SongWXZJ24","acl_id":null,"title":"Balancing Objective Optimization and Constraint Satisfaction in Expensive Constrained Evolutionary Multiobjective Optimization","year":2024,"publication_date":"2024-10-01","venue":"IEEE Transactions on Evolutionary Computation","journal":{"name":"IEEE Transactions on Evolutionary Computation","pages":"1286-1300","volume":"28"},"journal_issn":null,"journal_title":null,"publication_types":["JournalArticle"],"pubmed_pub_types":null,"s2_fields_of_study":["Computer Science","Engineering"],"reference_count":50,"citation_count":39,"influential_citation_count":0,"is_open_access":false,"arxiv_categories":null,"arxiv_license":null,"arxiv_journal_ref":null,"mesh_headings":null,"chemicals":null,"comments_corrections":null,"source_flags":1,"s2_open_access_pdf_url":null,"s2_open_access_landing_url":null,"s2_open_access_license":null,"s2_open_access_status":null,"pmc_open_access_pdf_url":null,"pmc_open_access_landing_url":null,"pmc_open_access_license":null,"pmc_open_access_status":null,"unpaywall_open_access_pdf_url":null,"unpaywall_open_access_landing_url":null,"unpaywall_open_access_license":null,"unpaywall_open_access_status":null,"abstract":"In dealing with expensive constrained multiobjective optimization problems using surrogate-assisted evolutionary algorithms, it is a great challenge to reduce the negative impact caused by the approximate errors of surrogate models for constraints. To address this issue, we propose a Kriging-assisted evolutionary algorithm with two search modes to adaptively reduce the utilization frequency of surrogate models for constraints. To be more specific, an adaptively switching strategy analyzing the correlation between the objective optimization direction and constraint satisfaction direction is designed to determine whether to build the constraint surrogate models to assist the current evolutionary search. Accordingly, the proposed algorithm contains two search modes: 1) unconstrained surrogate-assisted search mode and 2) constrained surrogate-assisted search mode. In the first search mode, an existing surrogate-assisted evolutionary algorithm without considering constraint is introduced, which rapidly drives the population to move to the feasible region(s) while avoiding the negative effects of the constraint surrogate models. In the second search mode, a novel Kriging-assisted constrained multiobjective optimization algorithm is designed for locating constrained Pareto front in the feasible region. In addition, a data selection strategy is proposed to improve the efficiency and quality of surrogate models for constraint functions. The proposed method has been tested on numerous instances from three popular benchmark test suites. The experimental results demonstrate that the performance of the proposed algorithm outperforms other state-of-the-art methods.","claims":[{"public_id":"cl_40d7ca951a75352dd06dd1ac21b85ea5","status":"active","text":"A data selection strategy is proposed to improve the efficiency and quality of surrogate models for constraint functions.","confidence":0.9,"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/claims/cl_40d7ca951a75352dd06dd1ac21b85ea5"},{"public_id":"cl_6aba23ece4233154cf079f9fae471510","status":"active","text":"The proposed Kriging-assisted evolutionary algorithm outperforms other state-of-the-art methods on three popular benchmark test suites.","confidence":0.9,"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/claims/cl_6aba23ece4233154cf079f9fae471510"},{"public_id":"cl_8d331b4181947bd322dfce868dca6d19","status":"active","text":"The proposed algorithm contains two search modes: unconstrained surrogate-assisted search mode and constrained surrogate-assisted search mode.","confidence":0.95,"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/claims/cl_8d331b4181947bd322dfce868dca6d19"},{"public_id":"cl_b4f40c7a9ca5b2035ee3479c9fae4ead","status":"active","text":"The proposed algorithm uses an adaptively switching strategy that analyzes the correlation between objective optimization direction and constraint satisfaction direction to decide whether to build constraint surrogate models.","confidence":0.95,"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/claims/cl_b4f40c7a9ca5b2035ee3479c9fae4ead"}],"concepts":[{"public_id":"co_1e1e056c8deac67e16709cd3dfdce42b","status":"active","name":"constrained surrogate-assisted search mode","description":"A search mode that builds constraint surrogate models to locate the constrained Pareto front in the feasible region.","types":["search mode"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_1e1e056c8deac67e16709cd3dfdce42b"},{"public_id":"co_31426baed8f8c2ef89f3a7e4543631a5","status":"active","name":"surrogate models for constraints","description":"Surrogate models specifically built to approximate constraint functions.","types":["model"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_31426baed8f8c2ef89f3a7e4543631a5"},{"public_id":"co_466356218cb20dd0df9bc7c7bc3aac8a","status":"active","name":"Kriging-assisted evolutionary algorithm","description":"An evolutionary algorithm that uses Kriging surrogate models to assist optimization of expensive constrained multiobjective problems.","types":["method"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_466356218cb20dd0df9bc7c7bc3aac8a"},{"public_id":"co_593daaf38c4566951093982ac7ad1368","status":"active","name":"constraint satisfaction direction","description":"The direction of satisfying constraints in the evolutionary search.","types":["concept"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_593daaf38c4566951093982ac7ad1368"},{"public_id":"co_7057e05ae4a253f4e35e033dc47bd320","status":"active","name":"objective optimization direction","description":"The direction of optimizing the objectives in the evolutionary search.","types":["concept"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_7057e05ae4a253f4e35e033dc47bd320"},{"public_id":"co_7928bfb5861080f30a7d631bcec95bfc","status":"active","name":"unconstrained surrogate-assisted search mode","description":"A search mode where constraint surrogate models are not built, allowing the population to move toward feasible regions without negative effects.","types":["search mode"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_7928bfb5861080f30a7d631bcec95bfc"},{"public_id":"co_917f04ef531368e5f3077728ba38c8f8","status":"active","name":"constrained Pareto front","description":"The Pareto front within the feasible region of a constrained multiobjective problem.","types":["solution concept"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_917f04ef531368e5f3077728ba38c8f8"},{"public_id":"co_c05f63dd6d2f34208c24edca0bb7e321","status":"active","name":"adaptively switching strategy","description":"A strategy that analyzes the correlation between objective optimization direction and constraint satisfaction direction to decide whether to build constraint surrogate models.","types":["strategy"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_c05f63dd6d2f34208c24edca0bb7e321"},{"public_id":"co_d2b96216e5e9022f81153cca07840596","status":"active","name":"three popular benchmark test suites","description":"The three benchmark test suites used to evaluate the proposed algorithm.","types":["evaluation setting"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_d2b96216e5e9022f81153cca07840596"},{"public_id":"co_d90307bc36e18b8bae21374b6f911963","status":"active","name":"expensive constrained multiobjective optimization problems","description":"Optimization problems with multiple objectives and constraints that are expensive to evaluate, motivating the use of surrogate models.","types":["problem class"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_d90307bc36e18b8bae21374b6f911963"},{"public_id":"co_df59b45495f2a3964fe4021de074e227","status":"active","name":"surrogate-assisted evolutionary algorithms","description":"A class of evolutionary algorithms that use surrogate models to approximate expensive functions during optimization.","types":["algorithm class"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_df59b45495f2a3964fe4021de074e227"},{"public_id":"co_f65292b9fd3a297f3ac17c540bf6b6c7","status":"active","name":"data selection strategy","description":"A strategy to improve the efficiency and quality of surrogate models for constraint functions.","types":["strategy"],"aliases":[],"contributors":[{"id":1165,"public_id":"ezd9qvkvax","public_label":"The Reverser‮ (ezd9qvkvax)","roles":["extraction"],"url":"https://sah.borca.ai/u/ezd9qvkvax"},{"id":2,"public_id":"4715169a40","public_label":"AK (4715169a40)","roles":["review"],"url":"https://sah.borca.ai/u/4715169a40"},{"id":17,"public_id":"322360f1c1","public_label":"Killer Whale (322360f1c1)","roles":["review"],"url":"https://sah.borca.ai/u/322360f1c1"}],"url":"https://sah.borca.ai/concepts/co_f65292b9fd3a297f3ac17c540bf6b6c7"}],"external_ids":{"DOI":"10.1109/TEVC.2023.3300181","ArXiv":null,"PubMed":null,"PubMedCentral":null,"MAG":null,"DBLP":"journals/tec/SongWXZJ24","ACL":null},"open_access":{"is_open_access":false,"pdf_url":null,"landing_url":"https://sah.borca.ai/papers/260397241","source":null,"pdf_url_source":null,"license":null,"reason":"pdf_url_not_indexed"},"reference_availability":{"status":"available","references_indexed":true,"full_text_available":false,"full_text_source":null,"count_basis":"semantic_scholar_metadata","extraction_status":"not_applicable","reason":null},"source":{"provider":"episteme2","base_corpus":"semantic_scholar_dump","freshness_mode":"unknown","basis":["semantic_scholar_metadata","postgres_metadata"],"limits":["paper metadata is based on indexed upstream scholarly datasets","claims and concepts are available only for extracted papers","absence of claims or concepts means no extracted graph data is available in this response"],"status":"available","degraded":false,"degraded_reasons":[],"diagnostics":{"status":"available","degraded":false,"degraded_reasons":[],"metadata_status":"available","graph_status":"available","abstract_status":"available"},"source_flags":1},"paper_id":630820,"paper_uid":"7fa0c99d-119e-4fac-b3e8-2aa9c36222e5","canonical_identity":{"paper_id":630820,"paper_uid":"7fa0c99d-119e-4fac-b3e8-2aa9c36222e5","identity_status":"available","lookup_basis":"semantic_scholar_external_id","compatibility_path":"corpus_id"},"url":"https://sah.borca.ai/papers/260397241"}