traveling salesperson problem using branch and bound method in ADA in detail stepwise
 The Traveling Salesperson Problem (TSP) is a classic algorithmic problem in the field of computer science and operations research. It focuses on optimization. In this problem, a salesperson is given a list of cities, and must determine the shortest possible route that allows them to visit each city once and return to their original location.

The Branch and Bound method is a popular approach for solving the TSP. Here's a step-by-step guide to solving the TSP using the Branch and Bound method in Ada:

1. **Initialization**

First, you need to create the initial data structures for the problem. This includes creating a graph to represent the cities and their connections, as well as a priority queue to store the partial solutions that will be generated during the search.

Here's some sample code to get you started:
```arduino
type City is (City_1, City_2, City_3, ..., City_n);
type Graph is array (City, City) of Float;
type Path is array (1 .. Max_Cities) of City;
type Queue_Element is record
   Path   : Path;
   Length : Float;
end record;
type Priority_Queue is array (1 .. Max_Queued_Elements) of Queue_Element;
```
2. **Create the Initial Solution**
The Traveling Salesperson Problem (TSP) is a classic optimization problem in which the goal is to find the best tour or route to visit a given set of cities, such that the total distance traveled is minimized. 

A branch and bound approach can be used to solve this problem. This approach works by first creating a tree structure, where each node represents a possible solution. At each node, a bound is set, which is the maximum allowed distance between any two cities in the tour. The algorithm then explores the tree by expanding each node and calculating the optimal tour for that node. If the bound is exceeded, the node is pruned and the algorithm continues exploring the tree. Once the tree is completely explored, the optimal tour is found and returned. 

The bound is initially set to be infinity, which means that no two cities in the tour can be more than infinity apart. Then, the algorithm expands the root node, which represents the empty tour, and calculates the optimal tour. This tour is then stored and the bound is updated to be the sum of the distances in the tour. The algorithm then continues exploring the tree by expanding the next node and calculating the optimal tour. This process is repeated until the bound is less than or equal to the sum of the distances in the tour, which indicates that the optimal tour has been found.