title: Adaptive Grouping Cloud Model Shuffled Frog Leaping Algorithm for Solving Continuous Optimization Problems.
abstract: 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 "elite strategy" 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.
authors.affiliation: School of Automotive Engineering, Dezhou University, Dezhou 253023, China.
authors.forename: Haorui
authors.initials: H
authors.lastname: Liu
authors.affiliation: Automotive Engineering College, Shandong Jiaotong University, Jinan 250023, China.
authors.forename: Fengyan
authors.initials: F
authors.lastname: Yi
authors.affiliation: School of Automotive Engineering, Dezhou University, Dezhou 253023, China.
authors.forename: Heli
authors.initials: H
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical; D011340:Problem Solving
references.citation: BMC Genomics. 2012;13 Suppl 3:S6
references.pmid: 22759615
references.citation: Science. 1983 May 13;220(4598):671-80
references.pmid: 17813860