I am currently working on a project (TSP) and am attempting to convert some simulated annealing pseudocode into Java. I have been successful in the past at converting pseudocode into Java code, however I am unable to convert this successfully.
The pseudocode is:
T0(T and a lowercase 0) Starting temperature
Iter Number of iterations
λ The cooling rate
1. Set T = T0 (T and a lowercase 0)
2. Let x = a random solution
3. For i = 0 to Iter-1
4. Let f = fitness of x
5. Make a small change to x to make x’
6. Let f’ = fitness of new point
7. If f’ is worse than f then
8. Let p = PR(f’, f, Ti (T with a lowercase i))
9. If p > UR(0,1) then
10. Undo change (x and f)
11. Else
12. Let x = x’
13. End if
14. Let Ti(T with a lowercase i) + 1 = λTi(λ and T with a lowercase i)
15. End for
Output: The solution x
If somebody could show me a basic mark-up of this in Java I would be extremely grateful - I just can't seem to figure it out!
I am working across multiple classes using a number of functions (which I will not list as it is irrelevant for what I am asking). I already have a smallChange()
method and a fitness
function - could there be a chance that I would need to create a number of different versions of said methods? For example, I have something like:
public static ArrayList<Integer> smallChange(ArrayList<Integer> solution){
//Code is here.
}
Could I possibly need another version of this method which accepts different parameters? Something along the lines of:
public static double smallChange(double d){
//Code is here.
}
All I require is a basic idea of how this would look when written in Java - I will be able to adapt it to my code once I know what it should look like in the correct syntax, but I cannot seem to get past this particular hurdle.
Simulated Annealing is a stochastic global search optimization algorithm. This means that it makes use of randomness as part of the search process. This makes the algorithm appropriate for nonlinear objective functions where other local search algorithms do not operate well.
Simulated annealing is a method for solving unconstrained and bound-constrained optimization problems. The method models the physical process of heating a material and then slowly lowering the temperature to decrease defects, thus minimizing the system energy.
A typical example is the traveling salesman problem, which belongs to the NP-complete class of problems. For these problems, there is a very effective practical algorithm called simulated annealing (thus named because it mimics the process undergone by misplaced atoms in a metal when its heated and then slowly cooled).
The basic code should look like this:
public class YourClass {
public static Solution doYourStuff(double startingTemperature, int numberOfIterations, double coolingRate) {
double t = startingTemperature;
Solution x = createRandomSolution();
double ti = t;
for (int i = 0; i < numberOfIterations; i ++) {
double f = calculateFitness(x);
Solution mutatedX = mutate(x);
double newF = calculateFitness(mutatedX);
if (newF < f) {
double p = PR(); // no idea what you're talking about here
if (p > UR(0, 1)) { // likewise
// then do nothing
} else {
x = mutatedX;
}
ti = t * coolingRate;
}
}
return x;
}
static class Solution {
// no idea what's in here...
}
}
Now as far as wanting different versions of smallChange() method - totally doable, but you have to read up on inheritance a little bit
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