# Particle Swarm OPTIMization Particle swarm optimization. The maximum is searched. ```r psoptim(FUN, n=100, max.loop=100, w=0.9, c1=0.2, c2=0.2, xmin, xmax, vmax=c(4,4), seed=10, anim=TRUE) ``` ## Arguments - `FUN`: the optimized function with a vector as parameter - `n`: number of particles - `max.loop`: maximal number of iterations - `w`: inertia weight - `c1`: coefficient of the self-recognition component - `c2`: coefficient of the social component - `xmin`: vector of position constraints - minimal values - `xmax`: vector of position constraints - maximal values - `vmax`: vector of velocity constraints in each direction - `seed`: seed for random values - `anim`: logical; if `TRUE` (dafault), animation of the optimization process is shown ## Details The `i`-th particle velocity `v` in `j`-th direction is calculated in `t` iteration according to: `v[ij](t+1) = w*v[ij](t) + c1*r1*(xP[ij](t) - x[ij](t)) +c2*r2*(xS[j](t) - x[ij](t)).` where: `r1` and `r2` are random values, `w` is inertia weight, `c1` is a coefficient of the self-recognition component and `c2` is a coefficient of the social component. `xP` denotes so far best position of the particle and `xS` - the best position among the swarm. The new position (coordinates) is calculated as: `x[ij](t+1) = x[ij](t) + v[ij](t+1).` In the current version of the package, the function works without checking the correctness of the given arguments. ## Returns A list with the two components: `sol` solution, i.e. the best set of parameters found. `val` the best fitness function found. ## References Abraham A, Guo H, Liu H. (2006) **Swarm Intelligence: Foundations, Perspectives and Applications** in Nedjah N, Mourelle L. (eds.): "Swarm Intelligent Systems", Springer, Berlin Heidelberg, pp. 3-25. Banks A, Vincent J, Anyakoha C. (2007) **A review of particle swarm optimization. Part I: background and development**. Natural Computing, vol. 6, No. 4, pp. 467-484. Dorigo M, Stutzle T. (2004) **Ant Colony Optimization**, MIT Press. Eberhart R, Yuhui S. (2001) **Particle swarm optimization: developments, applications and resources**, Congress on Evolutionary Computation. Seoul, Korea. ## Author(s) Krzysztof Ciupke, `` ## Examples ```r n <- 50 m.l <- 50 w <- 0.95 c1 <- 0.2 c2 <- 0.2 xmin <- c(-5.12, -5.12) xmax <- c(5.12, 5.12) vmax <- c(4, 4) g <- function(x){ -(20 + x[,1]^2 + x[,2]^2 - 10*(cos(2*pi*x[,1]) + cos(2*pi*x[,2]))) } psoptim(FUN=g, n=n, max.loop=m.l, w=w, c1=c1, c2=c2, xmin=xmin, xmax=xmax, vmax=vmax, seed=5, anim=FALSE) ```

psoptim function