A salesperson needs to visit four cities , and . The distances between the cities are given in the following distance matrix:
Perform a branch and bound demonstration to find the optimal Traveling Salesperson Problem (TSP) tour for these cities. In your demonstration:
(i) Compute an initial lower bound based on the Minimum Spanning Tree (MST) of all four nodes. (ii) Compute a more refined initial lower bound using the matrix reduction method (sum of row minima). (iii) Explore the branch and determine its cost. (iv) Explain how a branch could be pruned if an upper bound of is already known. (v) Identify the optimal tour and its total distance.
[8]A Traveling Salesman Problem involves five locations, , and . The distances between certain pairs of locations are given in the following table:
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A candidate tour has been identified as .
Perform one 2-opt swap on the given tour to attempt an improvement. Indicate the new route and its length, and state whether the tour length decreased.
[6]Consider a Traveling Salesperson Problem (TSP) with four cities: A, B, C, and D. The distances between the cities are given in the following table:
Using the Held–Karp dynamic programming algorithm, compute the exact optimal Traveling Salesperson Problem (TSP) cost for a tour starting and ending at city A. Outline the recurrence relations and show the computations for each step.
[6]This question assesses the ability to evaluate the performance of the nearest neighbour heuristic by comparing it with an exact optimal solution using percentage excess in the context of the Traveling Salesman Problem (TSP).
The length of the route obtained by the nearest neighbour heuristic is 80 units and the length of the exact optimal route for the same matrix is 80 units.
Calculate the percentage excess of the heuristic over the optimum and compare the performance of the heuristic with the optimal route.
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