COMBINING GENETIC ALGORITHM AND SIMULATED ANNEALING - A MOLECULAR-GEOMETRY OPTIMIZATION STUDY

We introduce a new hybrid approach to determine the ground state geome try of molecular systems. Firstly, we compared the ability of genetic algorithm (GA) and simulated annealing (SA) to find the lowest energy geometry of silicon clusters with six and 10 atoms. This comparison sh owed that GA exhibits fast initial convergence, but its performance de teriorates as it approaches the desired global extreme. Interestingly, SA showed a complementary convergence pattern, in addition to high ac curacy. Our new procedure combines selected features from GA and SA to achieve weak dependence on initial parameters, parallel search strate gy, fast convergence and high accuracy. This hybrid algorithm outperfo rms GA and SA by one order of magnitude for small silicon clusters (Si b and Si lo) Next, we applied the hybrid method to study the geometry of a 20-atom silicon cluster. It was able to find an original geometry , apparently lower in energy than those previously described in litera ture. In principle, our procedure can be applied successfully to any m olecular system. (C) 1998 Elsevier Science B.V.