{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:HNXHZH2PXFNUBHKI2KWKEYA33K","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":"7460c7e4c528206bb7cd2047e4361003c2db7ffef5a6bce4e63a52d9c51c0871","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2026-05-05T08:57:52Z","title_canon_sha256":"b53a122cc64fec22896a75438087249201e63b70a9d1e25a8d72bceb809b285d"},"schema_version":"1.0","source":{"id":"2605.13871","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.13871","created_at":"2026-05-17T23:39:19Z"},{"alias_kind":"arxiv_version","alias_value":"2605.13871v1","created_at":"2026-05-17T23:39:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13871","created_at":"2026-05-17T23:39:19Z"},{"alias_kind":"pith_short_12","alias_value":"HNXHZH2PXFNU","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"HNXHZH2PXFNUBHKI","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"HNXHZH2P","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:722d43fee43d4b94a58f9af5161f5b7b114ffd085d27e3d37f01818b8f1d3464","target":"graph","created_at":"2026-05-17T23:39:19Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Extensive experiments on benchmark high-dimensional and multimodal test functions demonstrate superior performance of IWSO in terms of convergence speed, solution quality, and robustness."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the matchmaker-guided influence strategy and adaptive elimination mechanism translate the social analogy into algorithmic improvements that hold without hidden parameter tuning or benchmark-specific biases."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"IWSO is a new metaheuristic using matchmaker-guided elite influence and adaptive elimination-reinitialization to achieve faster convergence and higher solution quality than GA, PSO, DE, and CS on high-dimensional benchmark functions."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"IWSO models Indian wedding matchmaking as a guided search that lets elite solutions steer weaker ones while eliminating poor performers to maintain diversity."}],"snapshot_sha256":"e550ccb584a6f9c9ec0dc115df24729b9fea14156d4f7cfb20e7c693c9e7cc79"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"4971d02752d700e27b0a7dde625acc89fd3b61e9d1ab97980668e739bbbdd85f"},"paper":{"abstract_excerpt":"This paper presents a novel population-based metaheuristic, Indian Wedding System Optimization (IWSO), inspired by the socio-cultural dynamics of traditional Indian weddings. IWSO models the matchmaking process driven by collaboration among families, candidates, and matchmakers as a guided, selective search framework for solving complex optimization problems. The algorithm introduces two key innovations: (i) a matchmaker-guided influence strategy, where elite solutions direct the evolution of weaker candidates, enhancing convergence without external parameters; and (ii) an adaptive elimination","authors_text":"Ashutosh Kumar Singh, Deepika Saxena, Jatinder Kumar, Jitendra Kumar, Kishu Gupta, Niharika Singh, Sakshi Patni, Vinaytosh Mishra","cross_cats":["cs.LG"],"headline":"IWSO models Indian wedding matchmaking as a guided search that lets elite solutions steer weaker ones while eliminating poor performers to maintain diversity.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2026-05-05T08:57:52Z","title":"Indian Wedding System Optimization (IWSO): A Novel Socially Inspired Metaheuristic with Operational Design and Analysis"},"references":{"count":28,"internal_anchors":0,"resolved_work":28,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"X.-S. Yang, Z. Cui, R. Xiao, A. H. Gandomi, and M. Karamanoglu, Swarm intelligence and bio-inspired computation: theory and applica- tions. Newnes, 2013","work_id":"eeec281f-5826-4e9a-9284-6fb97256d0a9","year":2013},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"A surrogate-assisted memetic algorithm for permutation-based combinatorial optimization problems,","work_id":"361f5655-9e2e-439b-9d22-345863cd0d0d","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Nature-inspired metaheuristic algorithms. luniver press, 2010,","work_id":"ef9175a5-cfab-4f70-add7-624a3eecc290","year":2010},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Evolutionary transfer optimization-a new frontier in evolutionary computation research,","work_id":"e66cbe59-53a6-4294-bbfc-28b0eca6eebf","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Ant colony optimization algorithms for dynamic optimization: A case study of the dynamic travelling salesperson problem [research frontier],","work_id":"7fedd0e7-a8d9-4dc4-9e0c-ca24c5ee264e","year":2020}],"snapshot_sha256":"a912d8ed23a87300457d0918f52b86e797dc46b13f6638cd6fe09c72006438ac"},"source":{"id":"2605.13871","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T07:09:01.881071Z","id":"ae888a8d-7c71-4301-a8b4-29644845ec19","model_set":{"reader":"grok-4.3"},"one_line_summary":"IWSO is a new metaheuristic using matchmaker-guided elite influence and adaptive elimination-reinitialization to achieve faster convergence and higher solution quality than GA, PSO, DE, and CS on high-dimensional benchmark functions.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"IWSO models Indian wedding matchmaking as a guided search that lets elite solutions steer weaker ones while eliminating poor performers to maintain diversity.","strongest_claim":"Extensive experiments on benchmark high-dimensional and multimodal test functions demonstrate superior performance of IWSO in terms of convergence speed, solution quality, and robustness.","weakest_assumption":"That the matchmaker-guided influence strategy and adaptive elimination mechanism translate the social analogy into algorithmic improvements that hold without hidden parameter tuning or benchmark-specific biases."}},"verdict_id":"ae888a8d-7c71-4301-a8b4-29644845ec19"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:16b569230deb07e9e80d734f20155645d50f0ea15b2a8c74ceb2c90bd6e2bd03","target":"record","created_at":"2026-05-17T23:39:19Z","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":"7460c7e4c528206bb7cd2047e4361003c2db7ffef5a6bce4e63a52d9c51c0871","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2026-05-05T08:57:52Z","title_canon_sha256":"b53a122cc64fec22896a75438087249201e63b70a9d1e25a8d72bceb809b285d"},"schema_version":"1.0","source":{"id":"2605.13871","kind":"arxiv","version":1}},"canonical_sha256":"3b6e7c9f4fb95b409d48d2aca2601bda9650020e765cee5cb83e1688eeeb5f55","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3b6e7c9f4fb95b409d48d2aca2601bda9650020e765cee5cb83e1688eeeb5f55","first_computed_at":"2026-05-17T23:39:19.319683Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:19.319683Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yXKx//U0WaGxthm8OVbXNKavfGVqpGb5ij3XivRxDf58YQRIGL6OCraLfS5UiiioRuth+GUvr/t1qGIk5KbcAQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:19.320473Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.13871","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:16b569230deb07e9e80d734f20155645d50f0ea15b2a8c74ceb2c90bd6e2bd03","sha256:722d43fee43d4b94a58f9af5161f5b7b114ffd085d27e3d37f01818b8f1d3464"],"state_sha256":"997dcff0832ba4f3a2d52b46432df4aea36e943c195030e00aff2691de284f92"}