森林可燃物热解动力学参数的优化计算是构建综合热解模型的关键步骤。传统的基于梯度的优化方法收敛速度快但全局寻优能力不足，基于“生物进化理论”的遗传算法具有全局寻优能力但收敛速度慢。本研究首先探讨了单纯的遗传算法对初始值设置的依赖，发现设定合适的初始值能够稳定计算结果，加快算法的收敛速度。针对初始值未知的情况，本文提出了将单纯的遗传算法与迭代算法相结合构建混合型遗传算法的流程。然后以樟子松松枝为例，采用热重分析仪开展了森林可燃物热解实验。假设可燃物热解失重过程遵循三步一级平行反应模型，通过对比单纯遗传算法和混合型遗传算法的收敛过程，发现混合型遗传算法能够快速地获取全局最优的动力学参数，显著地提高遗传算法的优化性能。
For thermal degradation of forest fuels, the optimization of kinetic parameters is a crucial step for the construction of comprehensive pyrolysis model. Traditional gradient-based optimization methods are characterized by strong converging speed, but with weak global optimization capability. The Darwinian survivalof-the-fittest theory based genetic algorithm (GA) is a good tool for global optimization, but with weak converging speed because of the general principles of this algorithm. In this study we evaluated the dependence of the pure GA on the setting of the initial values (IVs), and found that the use of the correct initial values accelerated the converging speed and stabilized the results of the GA. A hybrid genetic algorithm (HGA) was used when the IVs were unknown. This algorithm shares the merits of iterative algorithms and GA. Thermogravimetric experiments were performed using the branches of Pinus Sylvestris and the results were used to compare the converging performances of GA and HGA under the assumption of a three-step, first-order pyrolysis model. The results of these analyses verified the validity and reliability of the HGA