{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:18:20Z","timestamp":1777702700897,"version":"3.51.4"},"reference-count":0,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2014,6]]},"abstract":"<jats:p>\n                    Distribution network structure optimization is the important part of city network planning; it directly affects the reliability and safety of the electric operation. In order to optimize the traditional ant colony algorithm in distribution network structure optimization, this paper proposes a improved algorithm to solve the problems, such as long time calculating, system premature stagnation, invalid searching and so on. Based on traditional ant colony algorithm, this paper improves it by optimizing transition probability and volatile factor. In the design of transition, the gang control will be realized between two parameters \u03b1, \u03b2 and Maximum iterating times N\n                    <jats:sub>max<\/jats:sub>\n                    ; In the improvement of the volatile factor, the adaptability will be enhanced. Through the verification analysis of specific example, the results show that improved ant colony algorithm, compared with basic ant colony algorithm, increases the ability of global searching and constringency and accelerates the calculation speed of the algorithm. The modified algorithm is feasible and effective.\n                  <\/jats:p>","DOI":"10.3233\/ifs-130947","type":"journal-article","created":{"date-parts":[[2019,12,2]],"date-time":"2019-12-02T18:35:35Z","timestamp":1575311735000},"page":"2799-2804","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["The power distribution network structure optimization based on improved ant colony algorithm"],"prefix":"10.1177","volume":"26","author":[{"given":"Wei","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Business Administration, North China Electric Power University, Baoding, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiannan","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Business Administration, North China Electric Power University, Baoding, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2014,1]]},"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IFS-130947","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IFS-130947","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:36:37Z","timestamp":1777455397000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/IFS-130947"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,1]]},"references-count":0,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2014,6]]}},"alternative-id":["10.3233\/IFS-130947"],"URL":"https:\/\/doi.org\/10.3233\/ifs-130947","relation":{"is-cited-by":[{"id-type":"doi","id":"10.1155\/2015\/753712","asserted-by":"object"}]},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,1]]}}}