Alignment particle swarm optimization

Particle swarm optimization (PSO) simulates the boids' collective behaviors. The original biological background of boid should follow three basic simple steering behaviors: separation, alignment and cohesion. However, to promote a fast convergent speed, the velocity update manner of each boid omits the alignment rule, this may result premature convergence phenomenon. Therefore, in this paper, the alignment rule is added to the velocity update manner for optimizing the multi-modal numerical problems, in which each particle adjusts its moving direction according to the personal historical best position and the alignment direction. Furthermore, a mutation operator is also introduced to enhance the population diversity. Simulation results show the proposed algorithm is effective and efficient.

[1]  Bao Zhang,et al.  Layout optimization of satellite module using soft computing techniques , 2008, Appl. Soft Comput..

[2]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[3]  W ReynoldsCraig.  Flocks, herds and schools: A distributed behavioral model , 1987 .

[4]  Peng-Yeng Yin,et al.  Multi-objective task allocation in distributed computing systems by hybrid particle swarm optimization , 2007, Appl. Math. Comput..

[5]  Zhihua Cui,et al.  A Guaranteed Global Convergence Particle Swarm Optimizer , 2004, Rough Sets and Current Trends in Computing.

[6]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[7]  Wenbo Xu,et al.  Particle swarm optimization with particles having quantum behavior , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[8]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[9]  Z. Cui,et al.  A FAST PARTICLE SWARM OPTIMIZATION , 2006 .

[10]  Shih-Wei Lin,et al.  A hybrid watermarking technique applied to digital images , 2008, Appl. Soft Comput..

[11]  Kevin D. Seppi,et al.  The Kalman Swarm: A New Approach to Particle Motion in Swarm Optimization , 2004, GECCO.

[12]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[13]  Zhihua Cui,et al.  Predicted-Velocity Particle Swarm Optimization Using Game-Theoretic Approach , 2006, ICIC.

[14]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..