Travelling Salesman Case Study

INTRODUCTION

The Travelling Salesman Problem (TSP) is one of the hardest and famous studied optimization problems in Computer Science. The TSP has many applications in the industrial world like transportation, industrial design, planning, logistic and network routing chip designing and manufacturing. etc.

Given a set of cities and the cost of travel (or distance) between each possible pairs, the TSP ,is to find the best possible way of visiting all the cities exactly once and returning to the starting point that minimize the travel cost (or travel distance) that is, to find the optimal solution to reduce the total travel cost. But since it is an NP-hard problem it is difficult to solve.

As a solution for TSP, it will either try to obtain the

There are many crossover methods used. A.Aranganayaki [10] introduces a crossover method called Sequential Constructive Crossover which produces high quality results for TSP.

But there are some drawbacks in GA. As the GA executes sequentially by steps, the time required to compute is higher. Number of objectives to be satisfied is the yet another factor that further delays the execution. Another issue with sequential implementation is its memory utilization for storing large population. The run time memory requirements are also higher since the population. To deal with these issues Parallel genetic Algorithm is used where the genetic algorithm is parallelized to improve the computation.

Another way of parallelization proposed by Dino Keco a, Abdulhamit Subasi [11] is MapReduce model. The Map Reduce model provides a parallel design pattern for simplifying application developments in distributed environments. In MapReduce we can split a large problem space into small pieces and parallelize the execution using small tasks on the smaller space. Hence by parallelizing the genetic algorithm using parallel genetic algorithm and MapReduce framework nearest optimal solutions can be obtained within a reasonable

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