A parallel hybrid GA for combinatorial optimization using grid technology

In this paper, we consider a parallel hybrid-GA (PHGA) for solving large combinatorial optimization problem. The approach of our PHGA is based on the island model whereby islands of subpopulations are farmed to multiple processing nodes for execution. The PHGA was applied to solve the quadratic assignment problems (QAP) to demonstrate the potential effectiveness of the models. In particular, we concentrate on QAP benchmarks of high complexity for n ranging from 60 to 256. Our results show that a two-island PHGA which employs a simplistic elite migration between islands outperforms the serial hybrid-GA (SHGA) significantly. As the size and complexity of the problem increases, the advantage of the PHGA in terms of computation time and solution quality becomes more evident. This opens up a wide channel for further exploration on implementation of the PHGA in a grid computing environment.