{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:NLGG2OQLHYDH7NFATVXMK5YGMH","short_pith_number":"pith:NLGG2OQL","schema_version":"1.0","canonical_sha256":"6acc6d3a0b3e067fb4a09d6ec5770661f7e2766dbf789c7036d7b6f08aadcb84","source":{"kind":"arxiv","id":"1707.08767","version":1},"attestation_state":"computed","paper":{"title":"An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Caimin Wei, Erik Goodman, Han Huang, Jiajie Mo, Wenji Li, Xinye Cai, Yi Fang, Yugen You, Zhun Fan","submitted_at":"2017-07-27T07:59:31Z","abstract_excerpt":"This paper proposes an improved epsilon constraint-handling mechanism, and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions (RFS) in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible r"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1707.08767","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-07-27T07:59:31Z","cross_cats_sorted":[],"title_canon_sha256":"80d2e98cb87cf4b77047a8779be1cded463fbbd4d9f72f77dda6a4646267494f","abstract_canon_sha256":"2ae1cb1f7690d11e12faf5b4474b0803668bf0f43b7c0639e22e4bd1734e4309"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:35:03.721751Z","signature_b64":"W18zRFFLi34ZxjnMjOvQXJ7YwETf6s2RY2nnWhQNaibWDbh1oRYehdVzW5WPp7fKsGSA/+MuPt8oqnazwgMbAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6acc6d3a0b3e067fb4a09d6ec5770661f7e2766dbf789c7036d7b6f08aadcb84","last_reissued_at":"2026-05-18T00:35:03.721015Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:35:03.721015Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Improved Epsilon Constraint-handling Method in MOEA/D for CMOPs with Large Infeasible Regions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Caimin Wei, Erik Goodman, Han Huang, Jiajie Mo, Wenji Li, Xinye Cai, Yi Fang, Yugen You, Zhun Fan","submitted_at":"2017-07-27T07:59:31Z","abstract_excerpt":"This paper proposes an improved epsilon constraint-handling mechanism, and combines it with a decomposition-based multi-objective evolutionary algorithm (MOEA/D) to solve constrained multi-objective optimization problems (CMOPs). The proposed constrained multi-objective evolutionary algorithm (CMOEA) is named MOEA/D-IEpsilon. It adjusts the epsilon level dynamically according to the ratio of feasible to total solutions (RFS) in the current population. In order to evaluate the performance of MOEA/D-IEpsilon, a new set of CMOPs with two and three objectives is designed, having large infeasible r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.08767","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1707.08767","created_at":"2026-05-18T00:35:03.721129+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.08767v1","created_at":"2026-05-18T00:35:03.721129+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.08767","created_at":"2026-05-18T00:35:03.721129+00:00"},{"alias_kind":"pith_short_12","alias_value":"NLGG2OQLHYDH","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_16","alias_value":"NLGG2OQLHYDH7NFA","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_8","alias_value":"NLGG2OQL","created_at":"2026-05-18T12:31:31.346846+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NLGG2OQLHYDH7NFATVXMK5YGMH","json":"https://pith.science/pith/NLGG2OQLHYDH7NFATVXMK5YGMH.json","graph_json":"https://pith.science/api/pith-number/NLGG2OQLHYDH7NFATVXMK5YGMH/graph.json","events_json":"https://pith.science/api/pith-number/NLGG2OQLHYDH7NFATVXMK5YGMH/events.json","paper":"https://pith.science/paper/NLGG2OQL"},"agent_actions":{"view_html":"https://pith.science/pith/NLGG2OQLHYDH7NFATVXMK5YGMH","download_json":"https://pith.science/pith/NLGG2OQLHYDH7NFATVXMK5YGMH.json","view_paper":"https://pith.science/paper/NLGG2OQL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.08767&json=true","fetch_graph":"https://pith.science/api/pith-number/NLGG2OQLHYDH7NFATVXMK5YGMH/graph.json","fetch_events":"https://pith.science/api/pith-number/NLGG2OQLHYDH7NFATVXMK5YGMH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NLGG2OQLHYDH7NFATVXMK5YGMH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NLGG2OQLHYDH7NFATVXMK5YGMH/action/storage_attestation","attest_author":"https://pith.science/pith/NLGG2OQLHYDH7NFATVXMK5YGMH/action/author_attestation","sign_citation":"https://pith.science/pith/NLGG2OQLHYDH7NFATVXMK5YGMH/action/citation_signature","submit_replication":"https://pith.science/pith/NLGG2OQLHYDH7NFATVXMK5YGMH/action/replication_record"}},"created_at":"2026-05-18T00:35:03.721129+00:00","updated_at":"2026-05-18T00:35:03.721129+00:00"}