title: An adaptive hybrid algorithm based on particle swarm optimization and differential evolution for global optimization.
abstract: Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail.
authors.affiliation: China Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, China ; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China.
authors.forename: Xiaobing
authors.initials: X
authors.lastname: Yu
authors.affiliation: China Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, China ; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Cao
authors.affiliation: China Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, China.
authors.forename: Haiyan
authors.initials: H
authors.lastname: Shan
authors.affiliation: China Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, China.
authors.forename: Li
authors.initials: L
authors.lastname: Zhu
authors.affiliation: School of Mechanic and Electronic Engineering, Wuhan University of Technology, Wuhan 430070, China.
authors.forename: Jun
authors.initials: J
authors.lastname: Guo
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D008954:Models, Biological; D009716:Numerical Analysis, Computer-Assisted