Optimizing a multi-modal function with a two step anneal method.

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I have been working on a reliable optimization method for this crazy function.



I noticed that if I had a large variance in the random normal generator, the optimizer would jump all over the place but would not settle down in the best optimum. So I added in a second step that takes the best of the attempted values and uses them as a second set of start values. Then, a second smaller variance is used that does not allow for major jumps. The two step anneal function can be seen below.

    xm = mu[1]
	ym = mu[2]
	fm = f(xm,ym)
	x = rep(NA,n*2)
	y = rep(NA,n*2)
	fx = rep(NA,n*2)
	for(k in 1:2){
		for (i in 1:n){
			dxm = xm+rnorm(1,0,s)
			dym = ym+rnorm(1,0,s)
			fdm = f(dxm, dym)

			t = t*g
			if (runif(1) < (fdm/fm)^(1/t)){
				xm = dxm
				ym = dym
				fm = fdm
			x[(i+(k-1)*n)] = xm
			y[(i+(k-1)*n)] = ym
			fx[(i+(k-1)*n)] = fm
		ii = which(fx==max(fx, na.rm=TRUE))[1]
		best=c(x[ii], y[ii])
	list(x = x, y = y, fx = fx, best = best , fbest = fx[ii], t=t)

To run the function and see the path of the anneal function use this code.


z<-outer(x,y, f.egg)

aa<-twostep.anneal(f.egg, n=1000, sig=c(2, 2), mu=c(5,5), t=2, g=.99)

lines(aa$x, aa$y, col=2)

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