This document discusses using a hill climbing search algorithm to solve the traveling salesman problem (TSP). It begins by defining the TSP problem and explaining that it is NP-Hard. It then introduces stochastic optimization methods, including hill climbing, which take randomly generated routes and incrementally improve them. Hill climbing works by only taking steps that improve the current solution until no better steps can be found, risking getting stuck at local maxima.

1. Tackling the travelling salesman problem
Using Hill Climbing search algorithm

2. Agenda
•What is the TSP problem ?
•stochastic optimization
•hill-climbing

3. What is the TSP problem ?
The traveling salesman problem (TSP) asks for
the shortest route to visit a collection of cities
and return to the starting point. Despite an intensive
study by mathematicians, computer scientists, operations
researchers, and others, over the past 50 years, it remains
an open question whether or not an efficient general
solution method exists.

4. TSP is an NP-Hard Problem
That does not necessarily mean any one instance of the
problem will be hard to solve, it just means that we do not
currently have an algorithm that can give us the guaranteed
best solution for all problems in “polynomial time”.

5. Stochastic optimization
Stochastic optimization (SO) methods
are optimization methods that generate and use random
variables. For stochastic problems, the random variables
appear in the formulation of the optimization problem
itself, which involve random objective functions or random
constraints, for example. Stochastic optimization methods
also include methods with random iterates.

6. We take randomly generated routes through the cities
and incrementally improve/change them in some fashion
to search for a better route. How these changes are
made depends on the algorithm being used, but there
are a couple of simple approaches we can take, that I
will outline here:
• swapping the position of two cities on a route
• reversing the order of an entire section of a route

7. Hill-climbing

8. Solution landscapes
• A common way to visualize searching for solutions in an
optimization problem, such as the TSP, is to think of the
solutions existing within a “landscape”. Better solutions
exist higher up and we can take a step from one solution
to another in search of better solutions. How we make
steps will depend on the “move operators” we have
available and will therefore also affect how the
landscape “looks”. It will change which solutions are
“adjacent” to each other.

9. Solution landscapes
• For a simple optimization problem we can directly visual the solution
landscape:
The red dot represents our current solution. It should be pretty clear that if we simply carry
on going “uphill” we’ll get to the highest point in this solution landscape.
If we are using evolutionary optimization methods a solution landscape will often be referred
to as a fitness landscape.

10. Hill-climbing
• Hill-climbing, pretty much the simplest of
the stochastic optimisation methods, works like this:
• pick a place to start
• take any step that goes “uphill”
• if there are no more uphill steps, stop;
otherwise carry on taking uphill steps
• Metaphorically the algorithm climbs up a hill one step at a time.
It is a “greedy” algorithm and only ever takes steps that take it
uphill (though it can be adapted to behave differently). This
means that it is pretty quick to get to the top of a hill

11. Hill-climbing
As you can see our current solution (the red dot) can only
go downhill from it’s current position – yet it is not at the
highest point in the solution landscape.

12. Hill-climbing
The “biggest” hill in the solution landscape is known as
the global maximum. The top of any other hill is known as
a local maximum (it’s the highest point in the local area).
Standard hill-climbing will tend to get stuck at the top of a
local maximum, so we can modify our algorithm to restart
the hill-climb if need be. This will help hill-climbing find
better hills to climb – though it’s still a random search of
the initial starting points.

13. Thanks