title: An Enhanced Differential Evolution with Elite Chaotic Local Search.
abstract: Differential evolution (DE) is a simple yet efficient evolutionary algorithm for real-world engineering problems. However, its search ability should be further enhanced to obtain better solutions when DE is applied to solve complex optimization problems. This paper presents an enhanced differential evolution with elite chaotic local search (DEECL). In DEECL, it utilizes a chaotic search strategy based on the heuristic information from the elite individuals to promote the exploitation power. Moreover, DEECL employs a simple and effective parameter adaptation mechanism to enhance the robustness. Experiments are conducted on a set of classical test functions. The experimental results show that DEECL is very competitive on the majority of the test functions.
authors.affiliation: Institute of Medical Informatics and Engineering, School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China.
authors.forename: Zhaolu
authors.initials: Z
authors.lastname: Guo
authors.affiliation: School of Literature and Law, Jiangxi University of Science and Technology, Ganzhou 341000, China.
authors.forename: Haixia
authors.initials: H
authors.lastname: Huang
authors.affiliation: School of Information Science and Technology, Jiujiang University, Jiujiang 332005, China.
authors.forename: Changshou
authors.initials: C
authors.lastname: Deng
authors.affiliation: Institute of Medical Informatics and Engineering, School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China.
authors.forename: Xuezhi
authors.initials: X
authors.lastname: Yue
authors.affiliation: State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China.
authors.forename: Zhijian
authors.initials: Z
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005075:Biological Evolution; D003198:Computer Simulation; D006801:Humans; D017711:Nonlinear Dynamics