Journal of Electrical and Electronic Engineering
Volume 5, Issue 2, April 2017, Pages: 68-73
Received: Apr. 11, 2017;
Published: Apr. 12, 2017
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Jiao Weidong, School of Engineering, Zhejiang Normal University, Jinhua, China
Huang Zhijing, School of Engineering, Zhejiang Normal University, Jinhua, China
Yan Gongbiao, Department of Mechanical Engineering, Zhejiang University, Hangzhou, China
At the late evolution stage of the basic particle swarm optimization (BPSO), convergence process starts to slow down and the best fitness particle fluctuates around the globally-optimal solution, which may give rise to decrease on convergence precision of the BPSO. Therefore, an improved algorithm for particle swarm optimization was proposed. The modified version of PSO uses a controllable velocity-updating mode to control velocity of evolved particles, which is expected to be useful for tuning the search for the globally-optimal solution. Optimization examples showed that the improved PSO is superior to the BPSO, on not only convergence precision but also computation expense.
Improved Particle Swarm Optimization with Controllable Velocity-Updating Mode, Journal of Electrical and Electronic Engineering.
Vol. 5, No. 2,
2017, pp. 68-73.
Kennedy J, Eberhart R C. Particle swarm optimization. In Proc. of the IEEE Conf. on Neural Networks IV, Perth, IEEE Press, 1995, pp. 1942-1948.
Shi Y, Eberhart R C. A Modified Swarm Optimizer. IEEE International Conference of Evolutionary computation, Anchorage, Alaska, 1998, pp. 69-73.
Clerc M. The swarm and the queen: Towards a deterministic and adaptive particle swarm optimization. In Proc. of the ICEC. Washington, 1999, pp. 1951-1957.
Shi Y, Eberhart R C. Fuzzy adaptive particle swarm optimization. In Proc. of the Congress on Evolutionary Computation, Piscataway, IEEE, 2001, pp. 101-106.
Huang Y, Liu Y F, Peng Z M, et al. Research on particle swarm optimization algorithm with characteristic of quantum parallel and its application in parameter estimation for fractional-order chaotic systems. Acta Phys. Sin., 2015, 64 (3): 1-8.
Guo W H, Wang T S. Pre-Impact Configuration Optimization for a Space Robot Capturing Target Satellite. Journal of Astronautics, 2015, 36 (4): 390-396.
Zhai T T, Zhu J Q. New Method for First-Order Structure Design of Continuous Zoom Lens System. Acta Opt. Sin., 2015, 35 (7): 1-9.
Ireneusz G. A new approach to particle swarm optimization algorithm. Expert Systems with Applications, 2015, 42: 844-854.
Cheung N J, Ding X M, Shen H B. A supervised particle swarm algorithm for real-parameter optimization. Application Intelligence, 2015, 43: 825-839.
Tang R L, Fang Y J. Modification of particle swarm optimization with human simulated property. Neurocomputing, 2015, 153: 319-331.
Clerc M, Kennedy J. The particle swarm: Explosion stability and convergence in a multi-dimensional complex space. IEEE Trans. on Evolution Computer, 2002, 6 (1): 58-73.
Marini F, Walczak B. Particle swarm optimization (PSO): A tutorial. Chemometrics and Intelligent Laboratory Systems, 2015, 149: 153-165.