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## Fractional Order Darwinian Particle Swarm Optimization

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MatLab function for FDPSO (Fractional Order Darwinian Particle Swarm Optimization)

Updated

fdpso - MatLab function for FDPSO
Fractional Order Darwinian Particle Swarm Optimization

Limited to optimization problems of nine variables but can easily be extended many more variables.

xbest = fdpso(func)
xbest - solution of the optimization problem. The number of columns depends on the input func. size(func,2)=number of xi variables
func - string containing a mathematic expression. Variables are defined as xi. For instance, func='2*x1+3*x2' means that it is an optimization problem of two variables.

[xbest,fit] = fdpso(func)
fit - returns the optimized value of func using the xbest solution.

[xbest,fit] = fdpso(func,xmin)
xmin - minimum value of xi. size(xmin,2)=number of xi variables. Default -100.

[xbest,fit] = fdpso(func,xmin,xmax)
xmax - maximum value of xi. size(xmax,2)=number of xi variables. Default 100.

[xbest,fit] = fdpso(func,xmin,xmax,type)
type - minimization 'min' or maximization 'max' of the problem. Default 'min'.

[xbest,fit] = fdpso(func,xmin,xmax,type,population)
population - number of the swarm population. Default 30.

[xbest,fit] = fdpso(func,xmin,xmax,type,population,iterations)
iterations - number of iterations. Default 300.

Example: xbest = fdpso('10+5*x1^2-0.8*x2',[-10 -20],[20 40],'min')

Micael S. Couceiro
v1.0
13/11/2011

Original PSO developed by:
Kennedy, J. and Eberhart, R. C. (1995).
"Particle swarm optimization".
Proceedings of the IEEE 1995 International Conference on Neural Networks, pp. 1942-1948.

Original DPSO developed by:
Tillett, J., Rao, T., Sahin, F., Rao, R. (2005).
"Darwinian particle swarm optimization"
Proceedings of the 2nd Indian International Conference on Artificial Intelligence (IICAI-05).

Extended Fractional-Order DPSO developed by:
Couceiro, M. S., Ferreira, N. M. F. and Machado, J. A. T. (2011).
"Fractional Order Darwinian Particle Swarm Optimization"
Proceedings of the Symposium on Fractional Signals and Systems (FSS’11).