PSO algorithm (particle swarm optimization), using Python 2.7.

OBJECTIVE: Minimization of an objective function through PSO.

ALGORITHMS implemented:

1 - SPSO (Standard Particle Swarm Optimizer)
2 - PSO-WL (Linear Time Varying inercia Weight Particle Swarm Optimizer)
3 - PSO-WL-CL (Linear Time-Varying Inercia Weight with Time-Varying Acceleration Coefficients Particle Swarm Optimizer)
4 - PSO-WR (Random Time Varying Inertia Weight Particle Swarm Optimizer)
5 - PSO-WA (Adaptive Time-Varying Inertia Weight Particle Swarm Optimizer)
6 - HPSO (Self-Organizing Particle Swarm Optimizer)
7 - HPSO-CL (Linear Time-Varying Acceleration Coefficients Self-Organizing
Particle Swarm Optimizer)

RESTRICTION:
Only box restrictions are implemented.

STOP CRITERIA:
1 - maximum number of iterations
2 - standard deviation of particles
3 - optimum point changes

GRAPH:
1 - graphs of performance.
2 - graph of objective function.

MOVIE FRAMES:
1 - creation of frames for each iteration

REFERENCES:
[1] KENNEDY, J.; EBERHART, R. Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks. Anais... [S.l.]: IEEE. , 1995
[2] EBERHART, R.; KENNEDY, J. A new optimizer using particle swarm theory. MHS’95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science. Anais... [S.l.]: IEEE., 1995
[3] SHI, Y.; EBERHART, R. A modified particle swarm optimizer. 1998 IEEE International Conference on Evolutionary. Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360). Anais... [S.l.]: IEEE., 1998
[4] EBERHART, R. C. Particle swarm optimization: developments, applications and resources. Proceedings of the 2001.Congress on Evolutionary Computation (IEEE Cat. No.01TH8546). Anais... [S.l.]: IEEE. , 2001
[5] CLERC, M. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization.
[6] CLERC, M.; KENNEDY, J. The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, v. 6, n. 1, p. 58–73, 2002.
[7] ZHANG, L.; YU, H.; HU, S. Optimal choice of parameters for particle swarm optimization. Journal of Zhejiang University SCIENCE, v. 6A, n. 6, p. 528–534, jun 2005.
[8] XU, G. An adaptive parameter tuning of particle swarm optimization algorithm. Applied Mathematics and Computation, v. 219, n. 9, p. 4560–4569, jan 2013.
[9] SCHWAAB, M.; BISCAIA, J. . E. C.; MONTEIRO, J. L.; PINTO, J. C. Nonlinear parameter estimation through particle swarm   optimization. Chemical Engineering Science, v. 63, n. 6, p. 1542–1552, mar 2008. 
[10] RATNAWEERA, A.; HALGAMUGE, S. K.; WATSON, H. C. Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients. IEEE Transactions on Evolutionary Computation, v. 8, n. 3, p. 240–255, jun. 2004.
[11] EBERHART, R.; SHI, Y. Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512). IEEE, 2000. v. 1, n. 7, p. 84–88.