title: A Novel Particle Swarm Optimization Algorithm for Global Optimization.
abstract: Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms.
authors.affiliation: Department of Mathematics, Henan Normal University, Xinxiang 453007, China; Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control, School of Mathematics and Information Sciences, Henan Normal University, Xinxiang 453007, China.
authors.forename: Chun-Feng
authors.initials: CF
authors.lastname: Wang
authors.affiliation: Department of Mathematics, Henan Normal University, Xinxiang 453007, China.
authors.forename: Kui
authors.initials: K
authors.lastname: Liu
mesh_terms: D000465:Algorithms; D019985:Benchmarking; D003198:Computer Simulation; D006801:Humans; D008962:Models, Theoretical; D011340:Problem Solving