caa a22 4500 605470766 CHVBK 20210128100329.0 cr unu---uuuuu 210128e20151101xx s 000 0 eng 10.1007/s00500-014-1469-4 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1469-4 An improved PSO algorithm based on particle exploration forfunction optimization and the modeling of chaotic systems [Elektronische Daten] [Debao Chen, Jing Chen, Hao Jiang, Feng Zou, Tundong Liu] A novel method to improve the global performance of particle swarm optimization (PSO) is proposed, which extends the exploring domain of the optimal position in the current generation and the optimal position thus achieved by every particle. In each generation, the best two positions are modified according to their searching radii and directions. If the new solutions improve the old ones, the optimal positions in the updating equations of the conventional PSO algorithm will be replaced by the new solutions. Using this operator, the swarm diverges from local optimization easily. Moreover, the algorithm is easily implemented, and because the basic structure of PSO is not altered, the algorithm can be easily combined with different PSO methods to improve the performance. Some benchmark functions and chaotic systems are evaluated via simulations, showing that the proposed algorithm exceeds existing methods such as BPSO, LDWPSO, DNLPSO to some extent. Springer-Verlag Berlin Heidelberg, 2014 Particle swarm optimization (PSO) nationallicence Linear decreasing inertia weight PSO (LDWPSO) nationallicence Ladder PSO nationallicence Local convergence nationallicence Chaotic system nationallicence Chen Debao The College of Physics and Electronic Information, Huaibei Normal University, 235000, Huaibei, Anhui, China aut Chen Jing School of Information Science and Engineering, Xiamen University, 361005, Xiamen, China aut Jiang Hao School of Information Science and Engineering, Xiamen University, 361005, Xiamen, China aut Zou Feng The College of Physics and Electronic Information, Huaibei Normal University, 235000, Huaibei, Anhui, China aut Liu Tundong School of Information Science and Engineering, Xiamen University, 361005, Xiamen, China aut Soft Computing Springer Berlin Heidelberg 19/11(2015-11-01), 3071-3081 1432-7643 19:11<3071 2015 19 500 https://doi.org/10.1007/s00500-014-1469-4 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/s00500-014-1469-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chen Debao The College of Physics and Electronic Information, Huaibei Normal University, 235000, Huaibei, Anhui, China aut NATIONALLICENCE 700 1- Chen Jing School of Information Science and Engineering, Xiamen University, 361005, Xiamen, China aut NATIONALLICENCE 700 1- Jiang Hao School of Information Science and Engineering, Xiamen University, 361005, Xiamen, China aut NATIONALLICENCE 700 1- Zou Feng The College of Physics and Electronic Information, Huaibei Normal University, 235000, Huaibei, Anhui, China aut NATIONALLICENCE 700 1- Liu Tundong School of Information Science and Engineering, Xiamen University, 361005, Xiamen, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/11(2015-11-01), 3071-3081 1432-7643 19:11<3071 2015 19 500