{"created":"2023-07-27T06:24:43.703238+00:00","id":7753,"links":{},"metadata":{"_buckets":{"deposit":"e8e06c19-4a5f-4d2d-b204-216118fcf787"},"_deposit":{"created_by":3,"id":"7753","owners":[3],"pid":{"revision_id":0,"type":"depid","value":"7753"},"status":"published"},"_oai":{"id":"oai:kanazawa-u.repo.nii.ac.jp:00007753","sets":["2438:4190:4191"]},"author_link":["275","9858","2120","79292","9720","966"],"item_4_biblio_info_8":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2007-01-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"725","bibliographicPageEnd":"287","bibliographicPageStart":"280","bibliographicVolumeNumber":"73","bibliographic_titles":[{"bibliographic_title":"Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C"}]}]},"item_4_creator_33":{"attribute_name":"著者別表示","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"北山, 哲士"}],"nameIdentifiers":[{},{}]},{"creatorNames":[{"creatorName":"荒川, 雅生"}],"nameIdentifiers":[{},{},{}]},{"creatorNames":[{"creatorName":"山崎, 光悦"}],"nameIdentifiers":[{},{}]}]},"item_4_description_21":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"This paper proposes a new method which is called as Adaptive Range Particle Swarm Optimization (ARPSO), based on Adaptive Range Genetic Algorithm. That is, the active search domain is determined by using the mean and standard deviation of each design variable. In general, multipoints methods are utilized in the field of evolutionary computation. At the initial search stage it is preferable to explore the search domain widely, and is also preferable to explore the smaller search domain as the search goes on. To achieve this objective, new parameter which determines the active search domain is introduced. This new parameter gradually increases as the search goes on. Finally it is possible to shrink the search domain. The way to determine the maximum value of this new parameter is also shown in this paper. The optimum solution with high accuracy and a. little number of function calls is obtained by proposed method in compared with original Particle Swarm Optimization. Through numerical examples, the effectiveness and validity of proposed method are examined.","subitem_description_type":"Abstract"}]},"item_4_description_5":{"attribute_name":"提供者所属","attribute_value_mlt":[{"subitem_description":"金沢大学大学院自然科学研究科知的システム創成","subitem_description_type":"Other"},{"subitem_description":"金沢大学工学部","subitem_description_type":"Other"}]},"item_4_identifier_registration":{"attribute_name":"ID登録","attribute_value_mlt":[{"subitem_identifier_reg_text":"10.24517/00007740","subitem_identifier_reg_type":"JaLC"}]},"item_4_publisher_17":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"日本機械学会"}]},"item_4_relation_12":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type":"isIdenticalTo","subitem_relation_type_id":{"subitem_relation_type_id_text":"https://doi.org/10.1299/kikaic.73.280","subitem_relation_type_select":"DOI"}}]},"item_4_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0387-5024","subitem_source_identifier_type":"ISSN"}]},"item_4_text_2":{"attribute_name":"その他のタイトル","attribute_value_mlt":[{"subitem_text_value":"領域適応型Particle Swarm Optimizationの提案"}]},"item_4_version_type_25":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kitayama, Satoshi"}],"nameIdentifiers":[{},{},{},{}]},{"creatorNames":[{"creatorName":"Arakawa, Masao"}],"nameIdentifiers":[{},{}]},{"creatorNames":[{"creatorName":"Yamazaki, Koetsu"}],"nameIdentifiers":[{},{},{},{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2017-10-03"}],"displaytype":"detail","filename":"TE-PR-KITAYAMA-S-280.pdf","filesize":[{"value":"735.4 kB"}],"format":"application/pdf","licensetype":"license_11","mimetype":"application/pdf","url":{"label":"TE-PR-KITAYAMA-S-280.pdf","url":"https://kanazawa-u.repo.nii.ac.jp/record/7753/files/TE-PR-KITAYAMA-S-280.pdf"},"version_id":"1483cab8-16d2-4069-992a-e2ce745526ef"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Proposal of adaptive range particle swarm optimization","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Proposal of adaptive range particle swarm optimization"}]},"item_type_id":"4","owner":"3","path":["4191"],"pubdate":{"attribute_name":"公開日","attribute_value":"2017-10-03"},"publish_date":"2017-10-03","publish_status":"0","recid":"7753","relation_version_is_last":true,"title":["Proposal of adaptive range particle swarm optimization"],"weko_creator_id":"3","weko_shared_id":3},"updated":"2023-07-27T10:23:48.943667+00:00"}