{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:HNXHZH2PXFNUBHKI2KWKEYA33K","short_pith_number":"pith:HNXHZH2P","schema_version":"1.0","canonical_sha256":"3b6e7c9f4fb95b409d48d2aca2601bda9650020e765cee5cb83e1688eeeb5f55","source":{"kind":"arxiv","id":"2605.13871","version":1},"attestation_state":"computed","paper":{"title":"Indian Wedding System Optimization (IWSO): A Novel Socially Inspired Metaheuristic with Operational Design and Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"IWSO models Indian wedding matchmaking as a guided search that lets elite solutions steer weaker ones while eliminating poor performers to maintain diversity.","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Ashutosh Kumar Singh, Deepika Saxena, Jatinder Kumar, Jitendra Kumar, Kishu Gupta, Niharika Singh, Sakshi Patni, Vinaytosh Mishra","submitted_at":"2026-05-05T08:57:52Z","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"},"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":true,"formal_links_present":true},"canonical_record":{"source":{"id":"2605.13871","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NE","submitted_at":"2026-05-05T08:57:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b53a122cc64fec22896a75438087249201e63b70a9d1e25a8d72bceb809b285d","abstract_canon_sha256":"7460c7e4c528206bb7cd2047e4361003c2db7ffef5a6bce4e63a52d9c51c0871"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:19.320473Z","signature_b64":"yXKx//U0WaGxthm8OVbXNKavfGVqpGb5ij3XivRxDf58YQRIGL6OCraLfS5UiiioRuth+GUvr/t1qGIk5KbcAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b6e7c9f4fb95b409d48d2aca2601bda9650020e765cee5cb83e1688eeeb5f55","last_reissued_at":"2026-05-17T23:39:19.319683Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:19.319683Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Indian Wedding System Optimization (IWSO): A Novel Socially Inspired Metaheuristic with Operational Design and Analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"IWSO models Indian wedding matchmaking as a guided search that lets elite solutions steer weaker ones while eliminating poor performers to maintain diversity.","cross_cats":["cs.LG"],"primary_cat":"cs.NE","authors_text":"Ashutosh Kumar Singh, Deepika Saxena, Jatinder Kumar, Jitendra Kumar, Kishu Gupta, Niharika Singh, Sakshi Patni, Vinaytosh Mishra","submitted_at":"2026-05-05T08:57:52Z","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"},"claims":{"count":4,"items":[{"kind":"strongest_claim","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.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","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.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","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.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"IWSO models Indian wedding matchmaking as a guided search that lets elite solutions steer weaker ones while eliminating poor performers to maintain diversity.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"e550ccb584a6f9c9ec0dc115df24729b9fea14156d4f7cfb20e7c693c9e7cc79"},"source":{"id":"2605.13871","kind":"arxiv","version":1},"verdict":{"id":"ae888a8d-7c71-4301-a8b4-29644845ec19","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T07:09:01.881071Z","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.","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","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.","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."},"references":{"count":28,"sample":[{"doi":"","year":2013,"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","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"A surrogate-assisted memetic algorithm for permutation-based combinatorial optimization problems,","work_id":"361f5655-9e2e-439b-9d22-345863cd0d0d","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2010,"title":"Nature-inspired metaheuristic algorithms. luniver press, 2010,","work_id":"ef9175a5-cfab-4f70-add7-624a3eecc290","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Evolutionary transfer optimization-a new frontier in evolutionary computation research,","work_id":"e66cbe59-53a6-4294-bbfc-28b0eca6eebf","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"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","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":28,"snapshot_sha256":"a912d8ed23a87300457d0918f52b86e797dc46b13f6638cd6fe09c72006438ac","internal_anchors":0},"formal_canon":{"evidence_count":1,"snapshot_sha256":"4971d02752d700e27b0a7dde625acc89fd3b61e9d1ab97980668e739bbbdd85f"},"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":"2605.13871","created_at":"2026-05-17T23:39:19.319819+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.13871v1","created_at":"2026-05-17T23:39:19.319819+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13871","created_at":"2026-05-17T23:39:19.319819+00:00"},{"alias_kind":"pith_short_12","alias_value":"HNXHZH2PXFNU","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"HNXHZH2PXFNUBHKI","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"HNXHZH2P","created_at":"2026-05-18T12:33:37.589309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":1,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HNXHZH2PXFNUBHKI2KWKEYA33K","json":"https://pith.science/pith/HNXHZH2PXFNUBHKI2KWKEYA33K.json","graph_json":"https://pith.science/api/pith-number/HNXHZH2PXFNUBHKI2KWKEYA33K/graph.json","events_json":"https://pith.science/api/pith-number/HNXHZH2PXFNUBHKI2KWKEYA33K/events.json","paper":"https://pith.science/paper/HNXHZH2P"},"agent_actions":{"view_html":"https://pith.science/pith/HNXHZH2PXFNUBHKI2KWKEYA33K","download_json":"https://pith.science/pith/HNXHZH2PXFNUBHKI2KWKEYA33K.json","view_paper":"https://pith.science/paper/HNXHZH2P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.13871&json=true","fetch_graph":"https://pith.science/api/pith-number/HNXHZH2PXFNUBHKI2KWKEYA33K/graph.json","fetch_events":"https://pith.science/api/pith-number/HNXHZH2PXFNUBHKI2KWKEYA33K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HNXHZH2PXFNUBHKI2KWKEYA33K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HNXHZH2PXFNUBHKI2KWKEYA33K/action/storage_attestation","attest_author":"https://pith.science/pith/HNXHZH2PXFNUBHKI2KWKEYA33K/action/author_attestation","sign_citation":"https://pith.science/pith/HNXHZH2PXFNUBHKI2KWKEYA33K/action/citation_signature","submit_replication":"https://pith.science/pith/HNXHZH2PXFNUBHKI2KWKEYA33K/action/replication_record"}},"created_at":"2026-05-17T23:39:19.319819+00:00","updated_at":"2026-05-17T23:39:19.319819+00:00"}