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Momentum particle swarm optimizer

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4 Author(s)
Liu Yul ; Dept. of Computer Science, Xi'an Jiaotong Univ., XTan 710049, P. R. China ; Qin Zheng ; Wang Xianghua ; He Xingshi

The previous particle swarm optimizers lack direct mechanism to prevent particles beyond predefined search space, which results in invalid solutions in some special cases. A momentum factor is introduced into the original particle swarm optimizer to resolve this problem. Furthermore, in order to accelerate convergence, a new strategy about updating velocities is given. The resulting approach is mromentum-PSO which guarantees that particles are never beyond predefined search space without checking boundary in every iteration. In addition, linearly decreasing wight PSO (LDW-PSO) equipped with a boundary checking strategy is also discussed, which is denoted as LDWBOPSO. LDW-PSO, LDWBCPSO and momentum-PSO are compared in optimization on five test functions. The experimental results show that in some special cases LDW-PSO finds invalid solutions and LDWBC-PSO has poor performance, while momentum-PSO not only exhibits good performance but also reduces computational cost for updating velocities.

Published in:

Journal of Systems Engineering and Electronics  (Volume:16 ,  Issue: 4 )