A Comparative Study of Five Parallel Genetic Algorithms Using the Traveling Salesman Problem

Parallel genetic algorithms (PGAs) have been developed to reduce the large execution times that are associated with serial genetic algorithms (SGAs). They have also been used to solve larger problems and to find better solutions. In this paper, a comparative analysis of five different coarse-grained PGAs is conducted using the traveling salesman problem as the basis of this case study. To make fair comparisons, all of these PGAs are based on the same baseline SGA, implemented on the same parallel machine (IBM SP2), tested on the same set of traveling salesman problem instances, and started from the same set of initial populations. As a result of the experiments conducted in this study, a particular PGA that combines a new subtour technique with a known migration approach is identified to be the best for the traveling salesman problem among the five PGAs