{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T06:36:39Z","timestamp":1718174199469},"reference-count":28,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Info. Tech. Dec. Mak."],"published-print":{"date-parts":[[2010,3]]},"abstract":"<jats:p>In this paper, a novel clonal algorithm applied in multiobjecitve optimization (NCMO) is presented, which is designed from the improvement of search operators, i.e. dynamic mutation probability, dynamic simulated binary crossover (D-SBX) operator and hybrid mutation operator combining with Gaussian and polynomial mutations (GP-HM) operator. The main notion of these approaches is to perform more coarse-grained search at initial stage in order to speed up the convergence toward the Pareto-optimal front. Once the solutions are getting close to the Pareto-optimal front, more fine-grained search is performed in order to reduce the gaps between the solutions and the Pareto-optimal front. Based on this purpose, a cooling schedule is adopted in these approaches, reducing the parameters gradually to a minimal threshold, the aim of which is to keep a desirable balance between fine-grained search and coarse-grained search. By this means, the exploratory capabilities of NCMO are enhanced. When compared with various state-of-the-art multiobjective optimization algorithms developed recently, simulation results show that NCMO has remarkable performance.<\/jats:p>","DOI":"10.1142\/s0219622010003804","type":"journal-article","created":{"date-parts":[[2010,4,7]],"date-time":"2010-04-07T11:11:14Z","timestamp":1270638674000},"page":"239-266","source":"Crossref","is-referenced-by-count":20,"title":["APPLICATION OF NOVEL CLONAL ALGORITHM IN MULTIOBJECTIVE OPTIMIZATION"],"prefix":"10.1142","volume":"09","author":[{"given":"JIANYONG","family":"CHEN","sequence":"first","affiliation":[{"name":"College of Information Engineering, Shenzhen University, Nanhai Avenue 3688, Shenzhen, Guangdong 518060, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"QIUZHEN","family":"LIN","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shenzhen University, Nanhai Avenue 3688, Shenzhen, Guangdong 518060, P. R. 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