caa a22 4500 605477817 CHVBK 20210128100402.0 cr unu---uuuuu 210128e20151001xx s 000 0 eng 10.1007/s10994-015-5522-z doi (NATIONALLICENCE)springer-10.1007/s10994-015-5522-z Random drift particle swarm optimization algorithm: convergence analysis and parameter selection [Elektronische Daten] [Jun Sun, Xiaojun Wu, Vasile Palade, Wei Fang, Yuhui Shi] The random drift particle swarm optimization (RDPSO) algorithm is a PSO variant inspired by the free electron model in metal conductors placed in an external electric field. Based on the preliminary work on the RDPSO algorithm, this paper makes systematical analyses and empirical studies of the algorithm. Firstly, the motivation of the RDPSO algorithm is presented and the design of the particle's velocity equation is described in detail. Secondly, a comprehensive analysis of the algorithm is made in order to gain a deep insight into how the RDPSO algorithm works. It involves a theoretical analysis and the simulation of the stochastic dynamical behavior of a single particle in the RDPSO algorithm. The search behavior of the algorithm itself is also investigated in detail, by analyzing the interaction among the particles. Then, some variants of the RDPSO algorithm are presented by incorporating different random velocity components with different neighborhood topologies. Finally, empirical studies of the RDPSO algorithm are performed by using a set of benchmark functions from the CEC2005 benchmark suite. Based on the theoretical analysis of the particle's behavior, two methods of controlling the algorithmic parameters are employed, followed by an experimental analysis on how to select the parameter values, in order to obtain a satisfactory overall performance of the RDPSO algorithm and its variants in real-world applications. A further performance comparison between the RDPSO algorithm and other variants of PSO is made to prove the effectiveness of the RDPSO. The Author(s), 2015 Evolutionary computation nationallicence Optimization nationallicence Particle swarm optimization nationallicence Random motion nationallicence Sun Jun Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, No 1800, Lihu Avenue, 214122, Wuxi, Jiangsu, China aut Wu Xiaojun Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, No 1800, Lihu Avenue, 214122, Wuxi, Jiangsu, China aut Palade Vasile Department of Computing, Coventry University, Priory Street, CV1 5FB, Coventry, UK aut Fang Wei Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, No 1800, Lihu Avenue, 214122, Wuxi, Jiangsu, China aut Shi Yuhui Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, 215123, Suzhou, Jiangsu, China aut Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 345-376 0885-6125 101:1-3<345 2015 101 10994 https://doi.org/10.1007/s10994-015-5522-z text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-015-5522-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Sun Jun Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, No 1800, Lihu Avenue, 214122, Wuxi, Jiangsu, China aut NATIONALLICENCE 700 1- Wu Xiaojun Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, No 1800, Lihu Avenue, 214122, Wuxi, Jiangsu, China aut NATIONALLICENCE 700 1- Palade Vasile Department of Computing, Coventry University, Priory Street, CV1 5FB, Coventry, UK aut NATIONALLICENCE 700 1- Fang Wei Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, No 1800, Lihu Avenue, 214122, Wuxi, Jiangsu, China aut NATIONALLICENCE 700 1- Shi Yuhui Department of Electrical and Electronic Engineering, Xi'an Jiaotong-Liverpool University, 215123, Suzhou, Jiangsu, China aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 101/1-3(2015-10-01), 345-376 0885-6125 101:1-3<345 2015 101 10994