A heterogeneous framework for the global parallelisation of genetic algorithms

There is a big need for the parallelisation of genetic algorithms. In this paper, a heterogeneous framework for the global parallelisation of genetic algorithms is presented. The framework uses a static all-worker parallel programming paradigm based on collective communication. It follows the single program multiple data parallel programming model. It utilises the power of parallel machines by allowing multiple crossover and mutation operators being used within a single genetic algorithm. This mixture of operators can be applied to the strings of a population in parallel without changes to the canonical sequential genetic algorithm. These features help the parallel genetic algorithm in exploiting the search space efficiently and thoroughly when compared to the sequential genetic algorithm. The framework is instantiated with specific parameters to solve an NP-hard problem, the asymmetric travelling salesman problem. The results for the parallel genetic algorithm are very good in terms of solution quality. Also very good speedup and scalability results were achieved on the parallel machine.