{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T05:16:20Z","timestamp":1683350180612},"reference-count":23,"publisher":"IGI Global","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>The living mechanism has limited life in nature; it will age and die with time. This article describes that during the progressive process, the aging mechanism is very important to keep a swarm diverse. In the quantum behavior particle swarm (QPSO) algorithm, the particles are aged and the algorithm is prematurely convergent, the self-renewal mechanism of life is introduced into QPSO algorithm, and a leading particle and challengers are introduced. When the population particles are aged and the leading power of leading particle is exhausted, a challenger particle becomes the new leader particle through the competition update mechanism, group evolution is completed and the group diversity is maintained, and the global convergence of the algorithm is proven. Next in the article, twelve Clement2009 benchmark functions are used in the experimental test, both the comparison and analysis of results of the proposed method and classical improved QPSO algorithms are given, and the simulation results show strong global finding ability of the proposed algorithm. Especially in the seven multi-model test functions, the comprehensive performance is optimal.<\/jats:p>","DOI":"10.4018\/ijsir.2018010101","type":"journal-article","created":{"date-parts":[[2017,11,15]],"date-time":"2017-11-15T15:21:51Z","timestamp":1510759311000},"page":"1-19","source":"Crossref","is-referenced-by-count":2,"title":["A Quantum Particle Swarm Optimization Algorithm Based on Self-Updating Mechanism"],"prefix":"10.4018","volume":"9","author":[{"given":"Shuyue","family":"Wu","sequence":"first","affiliation":[{"name":"School of Information Science & Engineering, Hunan International Economics University, Changsha, China"}]}],"member":"2432","reference":[{"key":"IJSIR.2018010101-0","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2013.2290223"},{"key":"IJSIR.2018010101-1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2011.2173577"},{"key":"IJSIR.2018010101-2","doi-asserted-by":"publisher","DOI":"10.1109\/4235.985692"},{"key":"IJSIR.2018010101-3","doi-asserted-by":"publisher","DOI":"10.2307\/1943075"},{"key":"IJSIR.2018010101-4","author":"W.Fang","year":"2008","journal-title":"Swarm intelligence and its application In the optimal design of digital filters"},{"key":"IJSIR.2018010101-5","doi-asserted-by":"publisher","DOI":"10.4103\/0256-4602.64601"},{"key":"IJSIR.2018010101-6","doi-asserted-by":"publisher","DOI":"10.1142\/S179300570900143X"},{"issue":"2","key":"IJSIR.2018010101-7","first-page":"111","article-title":"Using quantum-behaved Particle swarm optimization for portfolio selection problem.","volume":"10","author":"S.Farzi","year":"2013","journal-title":"The International Arab Journal of Information Technology"},{"issue":"5","key":"IJSIR.2018010101-8","first-page":"379","article-title":"An evolutionary quantum behaved particle swarm optimization for mining association rules.","volume":"5","author":"K.Indiral","year":"2014","journal-title":"International Journal of Scientific & Engineering Research"},{"key":"IJSIR.2018010101-9","doi-asserted-by":"publisher","DOI":"10.3969\/j.issn.0372-2112.2016.12.013"},{"key":"IJSIR.2018010101-10","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1995.488968"},{"key":"IJSIR.2018010101-11","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-012-9330-6"},{"key":"IJSIR.2018010101-12","doi-asserted-by":"publisher","DOI":"10.11897\/SP.J.1016.2016.02429"},{"key":"IJSIR.2018010101-13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2009.03.006"},{"key":"IJSIR.2018010101-14","doi-asserted-by":"publisher","DOI":"10.1260\/1748-3018.9.2.143"},{"key":"IJSIR.2018010101-15","doi-asserted-by":"publisher","DOI":"10.1287\/moor.6.1.19"},{"key":"IJSIR.2018010101-16","author":"P.Suganthan","year":"2005","journal-title":"Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization"},{"key":"IJSIR.2018010101-17","doi-asserted-by":"publisher","DOI":"10.1162\/EVCO_a_00049"},{"key":"IJSIR.2018010101-18","unstructured":"Sun, J., Xu, W., & Feng, B. 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