Abstract: When standard genetic algorithm (SGA) is applied to numerical optimization, the convergence is general slow and it is easy to fall into local optimal solution with low accuracy. Aimed at the problems, an improved genetic algorithm (FGA) with detonation operator is proposed. The explosion operator (fire algorithm, FA) through the local optimal solution set of explosion of new individuals makes up for the defects of SGA algorithm and the insufficiency of population diversity.Therefore, the search ability of algorithm in global analytic domain is improved.The induction of the elitist strategy, for which the best individual of each generation can be preserved, avoids crossover and mutation the operating loss of the global optimal solution. To verify the optimization performance of the algorithm, four classical test functions were used to compare the optimization performance of the two algorithms: SGA and FGA. The results of the example show that the proposed algorithm has better global search capability, convergence performance and computational accuracy.