{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:25:32Z","timestamp":1777703132344,"version":"3.51.4"},"reference-count":23,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2015,8,10]],"date-time":"2015-08-10T00:00:00Z","timestamp":1439164800000},"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":[[2015,8,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms. However, the PSO converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima. To improve the diversity of the PSO, we here propose a memetic algorithm called particle swarm gravitation optimization (PSGO). After a specific number of iterations, some individuals selected from the PSO and GSA systems are exchanged by the roulette wheel approach. Finally, to increase the diversities of the PSO and GSA, we introduce a diversity enhancement operator, which is inspired by the crossover operator used in differential evolution algorithms. In evaluations of five benchmark functions, the PSGO significantly outperformed the PSO and Cuckoo search and yielded a superior performance to the GSA of most of instances and computation times.<\/jats:p>","DOI":"10.3233\/ifs-151543","type":"journal-article","created":{"date-parts":[[2015,8,21]],"date-time":"2015-08-21T15:28:07Z","timestamp":1440170887000},"page":"2655-2665","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":6,"title":["PSGO: Particle Swarm Gravitation Optimization Algorithm"],"prefix":"10.1177","volume":"28","author":[{"given":"Ko-Wei","family":"Huang","sequence":"first","affiliation":[{"name":"Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jui-Le","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C."},{"name":"Department of Computer Science and Entertainment Technology, Tajen University, Pingtung, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chu-Sing","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun-Wei","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, National Ilan University, Yilan, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2015,8,10]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-1665-5"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/3477.484436"},{"key":"e_1_3_1_4_2","first-page":"1942","article-title":"Particle swarm optimization","volume":"4","author":"Kennedy J","year":"1995","unstructured":"KennedyJEberhartR1995Particle swarm optimizationIn IEEE International Conference on Neural Networksvolume 419421948","journal-title":"In IEEE International Conference on Neural Networks"},{"key":"e_1_3_1_5_2","unstructured":"KarabogaD2005An idea based on Honey Bee Swarm for Numerical Optimization. Technical Report TR06 Erciyes University"},{"key":"e_1_3_1_6_2","doi-asserted-by":"crossref","unstructured":"YangX-S2009Firefly algorithms for multimodal optimizationStochastic Algorithms: Foundations and Applications volume 5792 of Lecture Notes in Computer ScienceWatanabeOZeugmannT169178Berlin HeidelbergSpringer","DOI":"10.1007\/978-3-642-04944-6_14"},{"key":"e_1_3_1_7_2","unstructured":"YangX-S2014Chapter 9 - cuckoo searchNature-Inspired Optimization AlgorithmsYangX-S129139ElsevierOxford"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2013.05.003"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2009.2030331"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2006.883272"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2008.07.021"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2010.02.032"},{"key":"e_1_3_1_13_2","first-page":"1","article-title":"A memetic particle swarm optimization algorithm for solving the dna fragment assembly problem","author":"Huang K-W","year":"2014","unstructured":"HuangK-WChenJ-LYangC-STsaiC-W2014A memetic particle swarm optimization algorithm for solving the dna fragment assembly problemNeural Computing and Applications112","journal-title":"Neural Computing and Applications"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pnucene.2008.07.002"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11721-007-0002-0"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2005.857610"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2010.2052054"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2012.10.012"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2009.03.004"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2006.10.002"},{"key":"e_1_3_1_21_2","unstructured":"GoldbergDE1989Genetic Algorithms in Search Optimization and Machine Learning1st editionBoston MA USAAddison-Wesley Longman Publishing Co. Inc"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008202821328"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.amc.2013.12.175"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2012.01.011"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IFS-151543","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/IFS-151543","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/IFS-151543","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:37:58Z","timestamp":1777455478000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/IFS-151543"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,8,10]]},"references-count":23,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2015,8,10]]}},"alternative-id":["10.3233\/IFS-151543"],"URL":"https:\/\/doi.org\/10.3233\/ifs-151543","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,8,10]]}}}