| A very effective way in identifying the global optimum solution to a difficult function optimization problem is to effectively combine Genetic Algorithm (GA) with Simulated Annealing (SA) and build a Hybrid Genetic Algorithms and Simulated Annealing (HGASA). We ever implemented a sequential HGASA and measured its performance; performance survey shows that it is a very good method as compared with some sequential optimization algorithms that offer low efficiency and limited reliability. However, the sequential HGASA generally needs a long run time cost. Thus we implemented a parallel HGASA using Message Passing Interface (MPI) on high performance computers and performed many tests using a set of frequently used function optimization problems. The detailed performance analysis of this parallel approach has been done as well in terms of program execution time, relative speed up and efficiency. The experimental results show that this parallel optimization method is a very suitable for tackling some complicated function optimization problems. |
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