Abstract: Simulated annealing algorithm is an artificial intelligence combinatorial optimization algorithm. Building upon the classic simulated annealing algorithm, we propose an enhanced version for predicting the molecular formulas of organic compounds. The algorithm begins by using a genetic algorithm to calculate the fitness values of individuals in the population, selecting the optimal individual as the initial solution for the simulated annealing process. Based on this initial solution, new solutions are generated through random perturbation, and their fitness values are calculated. If the change in fitness is less than or equal to zero, the new solution is accepted. Otherwise, the Metropolis criterion is applied to determine whether the new solution should be accepted. As the annealing temperature gradually decreases, the algorithm’s termination condition is used to determine if the global optimal solution has been found. Experimental results show that this improved algorithm increases the success rate of finding the global optimal solution. When applied to predict the molecular formulas of organic compounds, it demonstrates significantly better convergence of the fitness function compared to the classical simulated annealing algorithm.

Key words: Simulated annealing algorithm, Genetic algorithm, Combination optimization, Mass fraction, Molecular formulas