Abstract
Travelling Salesman Problem (TSP) is one of the most popular NP-complete problems for the researches in the field of computer science which focused on optimization. TSP goal is to find the minimum path between cities with a condition of each city must to visit exactly once by the salesman. Grey Wolf Optimizer (GWO) is a new swarm intelligent optimization mechanism where it success in solving many optimization problems. In this paper, a parallel version of GWO for solving the TSP problem on a Hypercube Interconnection Network is presented. The algorithm has been compared to the alternative algorithms. Algorithms have been evaluated analytically and by simulations in terms of execution time, optimal cost, parallel runtime, speedup and efficiency. The algorithms are tested on a number of benchmark problems and found parallel Gray wolf algorithm is promising in terms of speed-up, efficiency and quality of solution in comparison with the alternative algorithms.
Highlights
Due to the large increases in the number of cities in the world, mobility between cities has become difficult because of there existing many dissimilar roads to reach the same city with different travelling cost (Vukmirović and Pupavac, 2013), where there are several places that are all directly connected to each other by different long roads and the passenger wants to make the shortest trip
Since Grey Wolf Optimizer (GWO), CRO, and genetic algorithm (GA) are meta-heuristic mechanisms, the results obtained in different executions could be different
18.854 42.241 56.745 72.251 84.214 92.248 In Table 3, the first column shows the name of the instance
Summary
Due to the large increases in the number of cities in the world, mobility between cities has become difficult because of there existing many dissimilar roads to reach the same city with different travelling cost (Vukmirović and Pupavac, 2013), where there are several places that are all directly connected to each other by different long roads and the passenger wants to make the shortest trip. Some Algorithms can be used to guide people using one of the transport or movement methods (walking, train, car, and bus) to reach their destination on the shortest route. TSP is arisen in many different practical applications such as School bus routes, Computer wiring, job -shop scheduling and many more (Matai, et al, 2010). TSP has received great interest from researchers and mathematicians, as it is easy to describe, difficult to solve. If an efficient algorithm (polynomial time) can be found for solving TSP, efficient algorithms could be found for all others. (Karla, et al, 2016)
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