Explore Courses Blog Tutorials Interview Questions
0 votes
in AI and Deep Learning by (50.2k points)
edited by

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 that 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.

Thank you.

1 Answer

0 votes
by (108k points)

A Java-based approach to teaching simulated annealing (with sample code) is here:

Neller, Todd. Teaching Stochastic Local Search. It explains the functionality of Simulated Annealing perfectly using coding examples. The code which they provide can be easily adapted to any kind of optimization problem.

Related resources, references, and are here:

If you are looking to learn more about Java then you visit Java Tutorial. Also, if you are appearing for job profiles of Java Developer or Java Expert then you can prepare for the interviews on Java Interview Questions.

Browse Categories