Particle swarm optimization with voronoi neighborhood

Particle Swarm Optimization (PSO) is an optimization method that is inspired by nature and is used frequently nowadays. In this paper we proposed a new dynamic geometric neighborhood based on Voronoi diagram in PSO. Voronoi diagram is a geometric naturalistic method to determine neighbors in a set of particles. It seems that in realistic swarm, particles take Voronoi neighbors into account. Also a comparison is made between the performance of some traditional methods for choosing neighbors and new dynamic geometric methods like Voronoi and dynamic Euclidean. In this comparison it is found that PSO with geometric neighborhood can achieve better accuracy overall especially when the optimum value is out of the initial range.

[1]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[2]  J. Kennedy,et al.  Neighborhood topologies in fully informed and best-of-neighborhood particle swarms , 2003, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[3]  P. Suganthan.  Particle swarm optimiser with neighbourhood operator , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[4]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[5]  Y. Tan,et al.  Clonal particle swarm optimization and its applications , 2007, 2007 IEEE Congress on Evolutionary Computation.

[6]  Klaus Schilling,et al.  Voronoi strains - a spline path planning algorithm for complex environments , 2007, Artificial Intelligence and Applications.

[7]  Christian Posthoff,et al.  Neighborhood Re-structuring in Particle Swarm Optimization , 2005, Australian Conference on Artificial Intelligence.

[8]  P. Coles,et al.  Understanding recent observations of the largescale structure of the universe , 1990, Nature.

[9]  Joseph ORourke,et al.  Computational Geometry in C Second Edition , 1998 .

[10]  J. Kennedy,et al.  Population structure and particle swarm performance , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

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

[12]  Joseph O'Rourke,et al.  Computational Geometry in C. , 1995 .

[13]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[14]  Franz Aurenhammer,et al.  Voronoi diagrams—a survey of a fundamental geometric data structure , 1991, CSUR.

[15]  James Kennedy,et al.  Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[16]  José Neves,et al.  Watch thy neighbor or how the swarm can learn from its environment , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[17]  J. O´Rourke,et al.  Computational Geometry in C: Arrangements , 1998 .

[18]  Mark Richards,et al.  Choosing a starting configuration for particle swarm optimization , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[19]  Chu Kiong Loo,et al.  Quantum potential swarm optimization of PD controller for cargo ship steering , 2007 .

[20]  Andrew R. Webb,et al.  Statistical Pattern Recognition, Second Edition , 2002 .

[21]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[22]  A. S. Szalay,et al.  Large-scale distribution of galaxies at the Galactic poles , 1990, Nature.