[ANSWERED] write a Java Application for the following problem(provided in section-A) which involves finding…

write a Java Application for the following problem(provided in section-A) which involves finding the most efficient route through a collection of suburbs visiting each suburb exactly once. This is a classical optimization problem that can be applied to many other applications such as network routing. A brute force algorithm which systematically tries every possible solution is the only guaranteed method that can produce an optimal solution. By using brute force algorithm, the number of potential solutions experiences factorial growth. This means a problem with 5 suburbs will have 5! (120) potential solutions and with a problem with 10 suburbs that can be 3,628,800 solutions. Soon this can be over the available computation resources. The strategy is to use genetic algorithm to find a good enough solution near to optimal. A. The Problem Assume that you work for a product delivery company, where you need to deliver products to different suburbs of a city. In its simplest form, the suburbs can be represented by nodes on a graph, with the distance between suburbs represented by the length of the edges as shown in Figure 1. You can generate a set of suburbs by setting each suburb to a random x, y position in a 2D space. A route defines, commencing from a specific node (representing a suburb), which edges should be used, and what order, to reach back to the same node. The total distance travelled or the route length can be calculated by summing the length of the edges used in the route. This is a simpler simulation of a more complex problem where it is assumed that there exists a direct path between every pair of suburbs and travelling from one suburb to another in either direction takes the same distance, which is known as the symmetric travelling deliveryman problem. In a real-world situation this can be different if there are one-way routes between suburbs.
Attachments:
6353649_1_coit20277-assessment1-t12021-finalized-1.pdf
6353649_2_crossover-mutation-source-code-1.rtf
6353649_3_assignment-1—example-output-1.pdf