1. Simulated Annealing
G.Anuradha

2. What is it?
Simulated Annealing is a stochastic optimization method that derives its name from the annealing process used to re-crystallize metals
Comes under the category of evolutionary techniques of optimization

3. What is annealing?
Annealing is a heat process whereby a metal is heated to a specific temperature and then allowed to cool slowly. This softens the metal which means it can be cut and shaped more easily

4. What happens during annealing?
Initially when the metal is heated to high temperatures, the atoms have lots of space to move about
Slowly when the temperature is reduced the movement of free atoms are slowly reduced and finally the metals crystallize themselves

5. Relation between annealing and simulated annealing
Simulated annealing is analogous to this annealing process.
Initially the search area is more, there input parameters are searched in more random space and slow with each iteration this space reduces.
This helps in achieving global optimized value, although it takes much more time for optimizing

6. Analogy between annealing and simulated annealing
Energy in thermodynamic system
high-mobility atoms are trying to orient themselves with other nonlocal atoms and the energy state can occasionally go up.
low-mobility atoms can only orient themselves with local atoms and the energy state is not likely to go up again.
Simulated Annealing
Value of objective function
At high temperatures, SA allows fn. evaluations at faraway points and it is likely to accept a new point.
At low temperatures, SA evaluates the objective function only at local points and the likelihood of it accepting a new point with higher energy is much lower.

7. Cooling Schedule
how rapidly the temperature is lowered from high to low values.
This is usually application specific and requires some experimentation by trial-and-error.

8. Fundamental terminologies in SA
Objective function
Generating function
Acceptance function
Annealing schedule

9. Objective function
E = f(x), where each x is viewed as a point in an input space.
The task of SA is to sample the input space effectively to find an x that minimizes E.

10. Generating function
A generating function specifies the probability density function of the difference between the current point and the next point to be visited.
∆𝑥=(𝑥𝑛𝑒𝑤−𝑥)
is a random variable with probability density function g(∆x, T), where T is the temperature.

11. Acceptance function
Decides whether to accept/reject a new value of xnew
Where c – system dependent constant, T is temperature,
∆E is –ve SA accepts new point
∆E is +ve SA accepts with higher energy state
Initially SA goes uphill and downhill

12. Annealing schedule
decrease the temperature T by a certain percentage at each iteration.

13. Steps involved in general SA method

14. Steps involved in general SA method
Gaussian probability density function-Boltzmann machine is used in conventional GA

15. Travelling Salesman Problem
In a typical TSP problem there are ‘n’ cities, and the distance (or cost) between all pairs of these cities is an n x n distance (or cost) matrix D, where the element dij represents the distance (cost) of traveling from city i to city j.
The problem is to find a closed tour in which each city, except for starting one, is visited exactly once, such that the total length (cost) is minimized.
combinatorial optimization; it belongs to a class of problems known as NP-complete

16. TSP
Inversion: Remove two edges from the tour and replace them to make it another legal tour.

17. TSP
Translation Remove a section (8-7) of the tour and then replace it in between two randomly selected consecutive cities 4 and 5).

18. TSP
Switching: Randomly select two cities and switch them in the tour

19. Put together

20. SA(Extracts from Sivanandem)
Step 1:Initialize the vector x to a random point in the set φ
Step 2:Select an annealing schedule for the parameter T, and initialize T
Step 3:Compute xp=x+Δx where x is the proposed change in the system’s state
Step 4:Compute the change in the cool
Δf=f(xp)-f(x)

21. Algo contd….
Step 5: by using metropolis algorithm, decide if xp should be used as the new state of the system or the new state of the system or keep the current state x.
𝑝𝑟 𝑥→𝑥𝑝 = 1 𝑓𝑜𝑟 ∆𝑓<0 𝑒 − ∆𝑓 𝑇 𝑓𝑜𝑟 ∆𝑓≥0
Where T replaces kbT. When Δf>=0 a random number is selected from a uniform distribution in the range of [0 1]. If 𝑝𝑟(x xp) > n the state xp is used as the new state otherwise the state remains at x.

22. Algo contd…. Step 6: Repeat steps 3-5 n number of times
Step 7: If an improvement has been made after the n number of iterations, set the centre point of be the best point
Step 8:Reduce the temperature
Step 9: Repeat Steps 3-8 for t number of temperatures

23. Random Search
Explores the parameter space of an objective function sequentially in a random fashion to find the optimal point that maximizes or minimizes objective function
Simple
Optimization process takes a longer time

24. Primitive version (Matyas)

25. Observations in the primitive version
Leads to reverse step in the original method
Uses bias term as the center for random vector

26. Modified random search

27. Initial bias is chosen as a zero vector
Each component of dx should be a random variable having zero mean and variance proportional to the range of the corresponding parameter
This method is primarily used for continuous optimization problems