{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:Z3NADLTROS5LI6V52MZHNPJ4BE","short_pith_number":"pith:Z3NADLTR","schema_version":"1.0","canonical_sha256":"ceda01ae7174bab47abdd33276bd3c092181ab0c855ced0923fa35e88a5ac29e","source":{"kind":"arxiv","id":"1804.07995","version":1},"attestation_state":"computed","paper":{"title":"Global Convergence Analysis of the Flower Pollination Algorithm: A Discrete-Time Markov Chain Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"math.OC","authors_text":"Mehmet Karamanoglu, Xingshi He, Xin-She Yang, Yuxin Zhao","submitted_at":"2018-04-21T16:32:07Z","abstract_excerpt":"Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence"},"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":"1804.07995","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2018-04-21T16:32:07Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"c980f664fc6c321ac84e748d089a1e456cc90ab33d891d6cbd12b91e80826174","abstract_canon_sha256":"0634a0afd03c8c20d432d854d3e754bf0c492db7e39e9142f47d5028bd692e9e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:49.893443Z","signature_b64":"MNWwVm+lh/xCLfljkUaIWbrtt7BGWvDRjzYWV/DlulEhOPT7XL4A5a9vJ6Er8DNJETmvJo8iPHAVRO4HwHuwAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ceda01ae7174bab47abdd33276bd3c092181ab0c855ced0923fa35e88a5ac29e","last_reissued_at":"2026-05-18T00:17:49.892823Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:49.892823Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Global Convergence Analysis of the Flower Pollination Algorithm: A Discrete-Time Markov Chain Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"math.OC","authors_text":"Mehmet Karamanoglu, Xingshi He, Xin-She Yang, Yuxin Zhao","submitted_at":"2018-04-21T16:32:07Z","abstract_excerpt":"Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.07995","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":"1804.07995","created_at":"2026-05-18T00:17:49.892908+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.07995v1","created_at":"2026-05-18T00:17:49.892908+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.07995","created_at":"2026-05-18T00:17:49.892908+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z3NADLTROS5L","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z3NADLTROS5LI6V5","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z3NADLTR","created_at":"2026-05-18T12:33:04.347982+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/Z3NADLTROS5LI6V52MZHNPJ4BE","json":"https://pith.science/pith/Z3NADLTROS5LI6V52MZHNPJ4BE.json","graph_json":"https://pith.science/api/pith-number/Z3NADLTROS5LI6V52MZHNPJ4BE/graph.json","events_json":"https://pith.science/api/pith-number/Z3NADLTROS5LI6V52MZHNPJ4BE/events.json","paper":"https://pith.science/paper/Z3NADLTR"},"agent_actions":{"view_html":"https://pith.science/pith/Z3NADLTROS5LI6V52MZHNPJ4BE","download_json":"https://pith.science/pith/Z3NADLTROS5LI6V52MZHNPJ4BE.json","view_paper":"https://pith.science/paper/Z3NADLTR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.07995&json=true","fetch_graph":"https://pith.science/api/pith-number/Z3NADLTROS5LI6V52MZHNPJ4BE/graph.json","fetch_events":"https://pith.science/api/pith-number/Z3NADLTROS5LI6V52MZHNPJ4BE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z3NADLTROS5LI6V52MZHNPJ4BE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z3NADLTROS5LI6V52MZHNPJ4BE/action/storage_attestation","attest_author":"https://pith.science/pith/Z3NADLTROS5LI6V52MZHNPJ4BE/action/author_attestation","sign_citation":"https://pith.science/pith/Z3NADLTROS5LI6V52MZHNPJ4BE/action/citation_signature","submit_replication":"https://pith.science/pith/Z3NADLTROS5LI6V52MZHNPJ4BE/action/replication_record"}},"created_at":"2026-05-18T00:17:49.892908+00:00","updated_at":"2026-05-18T00:17:49.892908+00:00"}