New parallel hybrid genetic algorithm based on molecular dynamics approach for energy minimization of atomistic systems

A hybrid genetic algorithm (HGA) for the optimization of the ground state structure of a metallic atomic cluster has been implemented on a MIMD-SIMD parallel platform. The concept of building block (BB) is generalized to cover this real coded optimization problem. On the basis of some reasonings on the dependence of the convergence of genetic algorithms (GAs) from BBs, a hybrid genetic algorithm (HGA) is proposed to solve the minimization problem. All the elements of each new population are optimized through a molecular dynamics algorithm: the aim of MD is to create ever better BBs and, consequently, to improve the convergence of GAs. HGA has been implemented on a MIMD-SIMD platform based on the massively parallel processing supercomputer Quadrics/APE100, which offers a peak performance of 25.6 Gflops; we obtained a sustained computational power greater than 10 Gflops.