{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T23:30:34Z","timestamp":1764977434369,"version":"3.46.0"},"reference-count":30,"publisher":"Walter de Gruyter GmbH","issue":"3","license":[{"start":{"date-parts":[[2015,3,25]],"date-time":"2015-03-25T00:00:00Z","timestamp":1427241600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The problem of coverage optimization is the challengingly important and key part in the research and application of ecology sensor network related with the ecological monitoring of Poyang Lake. A modified differential evolution algorithm (PSI-DE) combined with particle swarm intelligence is proposed to solve the coverage optimization problem. First, an improved version of the mutation rule combined with self-cognitive and social-cognitive items is introduced. Then, the influence on the coverage optimization performance of the PSI-DE algorithm brought by the five factors \u2013 namely, population size, number of iterations, sensing radius size, raster size, and number of nodes \u2013 is discussed and analyzed. The statistical results about the best coverage rate, average coverage rate, worst coverage rate, and variance are respectively obtained through a lot of simulation experiments. A series of the coverage rate curves, the line chart, and the node layout are drawn in this paper, and finally, the figures and the statistical results are proven to confirm each other.<\/jats:p>","DOI":"10.1515\/jisys-2014-0133","type":"journal-article","created":{"date-parts":[[2015,3,25]],"date-time":"2015-03-25T11:41:29Z","timestamp":1427283689000},"page":"335-350","source":"Crossref","is-referenced-by-count":2,"title":["The Particle Swarm Differential Evolution Algorithm for Ecological Sensor Network Coverage Optimization"],"prefix":"10.1515","volume":"25","author":[{"given":"Xing","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Information and Engineering, Jingdezhen Ceramic Institute, Jiangxi 333403, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiqin","family":"Ying","sequence":"additional","affiliation":[{"name":"School of Software, South China University of Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Educational Software, Guangzhou University, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Information and Engineering, Jingdezhen Ceramic Institute, Jiangxi 333403, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2015,3,25]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"C.-F. 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