{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:GSEE74DG3273NQUQ4FKKWFPMXI","short_pith_number":"pith:GSEE74DG","canonical_record":{"source":{"id":"2211.15737","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"math.OC","submitted_at":"2022-11-28T19:36:55Z","cross_cats_sorted":[],"title_canon_sha256":"751b12c4dec0171c80a40e2bb64f2bc51ed345436f7c316a9ecc5c3a672a4a80","abstract_canon_sha256":"0593e5b44a30eee668f1cda02fe0e073a6f63c52e1b45583acad784f39a681f9"},"schema_version":"1.0"},"canonical_sha256":"34884ff066debfb6c290e154ab15ecba2f7c07a6ecbcc5ab4197036803258072","source":{"kind":"arxiv","id":"2211.15737","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.15737","created_at":"2026-07-05T05:20:17Z"},{"alias_kind":"arxiv_version","alias_value":"2211.15737v1","created_at":"2026-07-05T05:20:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.15737","created_at":"2026-07-05T05:20:17Z"},{"alias_kind":"pith_short_12","alias_value":"GSEE74DG3273","created_at":"2026-07-05T05:20:17Z"},{"alias_kind":"pith_short_16","alias_value":"GSEE74DG3273NQUQ","created_at":"2026-07-05T05:20:17Z"},{"alias_kind":"pith_short_8","alias_value":"GSEE74DG","created_at":"2026-07-05T05:20:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:GSEE74DG3273NQUQ4FKKWFPMXI","target":"record","payload":{"canonical_record":{"source":{"id":"2211.15737","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"math.OC","submitted_at":"2022-11-28T19:36:55Z","cross_cats_sorted":[],"title_canon_sha256":"751b12c4dec0171c80a40e2bb64f2bc51ed345436f7c316a9ecc5c3a672a4a80","abstract_canon_sha256":"0593e5b44a30eee668f1cda02fe0e073a6f63c52e1b45583acad784f39a681f9"},"schema_version":"1.0"},"canonical_sha256":"34884ff066debfb6c290e154ab15ecba2f7c07a6ecbcc5ab4197036803258072","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:20:17.543330Z","signature_b64":"Ozzx4KAYlsGptxrKsVWjxOHjuIyxllRm+Yo4B8CTB+VHxMMjX65lAEHD7bEEGp353syFg38MGIyqo8OEDuNbDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34884ff066debfb6c290e154ab15ecba2f7c07a6ecbcc5ab4197036803258072","last_reissued_at":"2026-07-05T05:20:17.542909Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:20:17.542909Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2211.15737","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:20:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EB1CfrOfF7hDzfBv63sgt4suxnmBNOAHkRGPZLd/29Ya0QQ8kAZUMWW1HCovl6KggwfTxkdM0umEtxIuX8GuDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T09:57:54.970147Z"},"content_sha256":"f13cb30d4c31ea03b2990d62f565e1bb0b03357fc8daf06973cdb151e7d9b01e","schema_version":"1.0","event_id":"sha256:f13cb30d4c31ea03b2990d62f565e1bb0b03357fc8daf06973cdb151e7d9b01e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:GSEE74DG3273NQUQ4FKKWFPMXI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Consensus-Based Optimization for Multi-Objective Problems: A Multi-Swarm Approach","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Claudia Totzeck, Kathrin Klamroth, Michael Stiglmayr","submitted_at":"2022-11-28T19:36:55Z","abstract_excerpt":"We propose a multi-swarm approach to approximate the Pareto front of general multi-objective optimization problems that is based on the Consensus-based Optimization method (CBO). The algorithm is motivated step by step beginning with a simple extension of CBO based on fixed scalarization weights. To overcome the issue of choosing the weights we propose an adaptive weight strategy in the second modelling step. The modelling process is concluded with the incorporation of a penalty strategy that avoids clusters along the Pareto front and a diffusion term that prevents collapsing swarms. Altogethe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.15737","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2211.15737/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:20:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U5RtNldFonCMWhTmuHsZc63TTU0XOifLbPvoTssCuYmZ7nmHeEUsrX+6vs7J0H9HsfZbLn0RXAkdyCFcORzjCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T09:57:54.971077Z"},"content_sha256":"d741cb4a59a9bc67d8811abee658110c5efe89aa6fae951824265a53ccbeba2a","schema_version":"1.0","event_id":"sha256:d741cb4a59a9bc67d8811abee658110c5efe89aa6fae951824265a53ccbeba2a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GSEE74DG3273NQUQ4FKKWFPMXI/bundle.json","state_url":"https://pith.science/pith/GSEE74DG3273NQUQ4FKKWFPMXI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GSEE74DG3273NQUQ4FKKWFPMXI/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-05T09:57:54Z","links":{"resolver":"https://pith.science/pith/GSEE74DG3273NQUQ4FKKWFPMXI","bundle":"https://pith.science/pith/GSEE74DG3273NQUQ4FKKWFPMXI/bundle.json","state":"https://pith.science/pith/GSEE74DG3273NQUQ4FKKWFPMXI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GSEE74DG3273NQUQ4FKKWFPMXI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:GSEE74DG3273NQUQ4FKKWFPMXI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"0593e5b44a30eee668f1cda02fe0e073a6f63c52e1b45583acad784f39a681f9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"math.OC","submitted_at":"2022-11-28T19:36:55Z","title_canon_sha256":"751b12c4dec0171c80a40e2bb64f2bc51ed345436f7c316a9ecc5c3a672a4a80"},"schema_version":"1.0","source":{"id":"2211.15737","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.15737","created_at":"2026-07-05T05:20:17Z"},{"alias_kind":"arxiv_version","alias_value":"2211.15737v1","created_at":"2026-07-05T05:20:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.15737","created_at":"2026-07-05T05:20:17Z"},{"alias_kind":"pith_short_12","alias_value":"GSEE74DG3273","created_at":"2026-07-05T05:20:17Z"},{"alias_kind":"pith_short_16","alias_value":"GSEE74DG3273NQUQ","created_at":"2026-07-05T05:20:17Z"},{"alias_kind":"pith_short_8","alias_value":"GSEE74DG","created_at":"2026-07-05T05:20:17Z"}],"graph_snapshots":[{"event_id":"sha256:d741cb4a59a9bc67d8811abee658110c5efe89aa6fae951824265a53ccbeba2a","target":"graph","created_at":"2026-07-05T05:20:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2211.15737/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a multi-swarm approach to approximate the Pareto front of general multi-objective optimization problems that is based on the Consensus-based Optimization method (CBO). The algorithm is motivated step by step beginning with a simple extension of CBO based on fixed scalarization weights. To overcome the issue of choosing the weights we propose an adaptive weight strategy in the second modelling step. The modelling process is concluded with the incorporation of a penalty strategy that avoids clusters along the Pareto front and a diffusion term that prevents collapsing swarms. Altogethe","authors_text":"Claudia Totzeck, Kathrin Klamroth, Michael Stiglmayr","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"math.OC","submitted_at":"2022-11-28T19:36:55Z","title":"Consensus-Based Optimization for Multi-Objective Problems: A Multi-Swarm Approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.15737","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f13cb30d4c31ea03b2990d62f565e1bb0b03357fc8daf06973cdb151e7d9b01e","target":"record","created_at":"2026-07-05T05:20:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"0593e5b44a30eee668f1cda02fe0e073a6f63c52e1b45583acad784f39a681f9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"math.OC","submitted_at":"2022-11-28T19:36:55Z","title_canon_sha256":"751b12c4dec0171c80a40e2bb64f2bc51ed345436f7c316a9ecc5c3a672a4a80"},"schema_version":"1.0","source":{"id":"2211.15737","kind":"arxiv","version":1}},"canonical_sha256":"34884ff066debfb6c290e154ab15ecba2f7c07a6ecbcc5ab4197036803258072","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"34884ff066debfb6c290e154ab15ecba2f7c07a6ecbcc5ab4197036803258072","first_computed_at":"2026-07-05T05:20:17.542909Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:20:17.542909Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Ozzx4KAYlsGptxrKsVWjxOHjuIyxllRm+Yo4B8CTB+VHxMMjX65lAEHD7bEEGp353syFg38MGIyqo8OEDuNbDw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:20:17.543330Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.15737","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f13cb30d4c31ea03b2990d62f565e1bb0b03357fc8daf06973cdb151e7d9b01e","sha256:d741cb4a59a9bc67d8811abee658110c5efe89aa6fae951824265a53ccbeba2a"],"state_sha256":"6228453ca91a00b9cfa23c98b7d6e9409516624f7e61da434088da5d9fdee478"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fbb5JrSs9vYyBRWcRbnF+Ravt2OjHtvNYxHCtg6pvq2yB597yTcl9P9SWtuEUqGFeDSMsGg+NDlr7tGnNUjJBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T09:57:54.978336Z","bundle_sha256":"8e880d5a3e4e7833d2f57613d9c502f3b890a699b377f5741e7df75a98b49c9c"}}