{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:06:17Z","timestamp":1761807977968},"reference-count":36,"publisher":"Hindawi Limited","license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shandong Province Crucial R&D Plan Project, China","award":["2015GGX105008","2014GGX105001"],"award-info":[{"award-number":["2015GGX105008","2014GGX105001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>The shuffled frog leaping algorithm (SFLA) easily falls into local optimum when it solves multioptimum function optimization problem, which impacts the accuracy and convergence speed. Therefore this paper presents grouped SFLA for solving continuous optimization problems combined with the excellent characteristics of cloud model transformation between qualitative and quantitative research. The algorithm divides the definition domain into several groups and gives each group a set of frogs. Frogs of each region search in their memeplex, and in the search process the algorithm uses the \u201celite strategy\u201d to update the location information of existing elite frogs through cloud model algorithm. This method narrows the searching space and it can effectively improve the situation of a local optimum; thus convergence speed and accuracy can be significantly improved. The results of computer simulation confirm this conclusion.<\/jats:p>","DOI":"10.1155\/2016\/5675349","type":"journal-article","created":{"date-parts":[[2015,12,27]],"date-time":"2015-12-27T21:03:21Z","timestamp":1451250201000},"page":"1-8","source":"Crossref","is-referenced-by-count":13,"title":["Adaptive Grouping Cloud Model Shuffled Frog Leaping Algorithm for Solving Continuous Optimization Problems"],"prefix":"10.1155","volume":"2016","author":[{"given":"Haorui","family":"Liu","sequence":"first","affiliation":[{"name":"School of Automotive Engineering, Dezhou University, Dezhou 253023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengyan","family":"Yi","sequence":"additional","affiliation":[{"name":"Automotive Engineering College, Shandong Jiaotong University, Jinan 250023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heli","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Automotive Engineering, Dezhou 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