The generalized gamma distribution (GGD) is a popular distribution because it is extremely flexible. Due to the density function structure of GGD, estimating the parameters of the GGD family by statistical point estimation techniques is a complicated task. In other words, for the parameter estimation, the maximizing likelihood function of GGD is a problematic case. Hence, alternative approaches can be used to obtain estimators of GGD parameters. This paper proposes an alternative parameter estimation method for GGD by using the heuristic optimization approaches such as Genetic Algorithms (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). A comparison between different modern heuristic optimization methods applied to maximize the likelihood function for parameter estimation is presented in this paper. The paper also investigates both the performance of heuristic methods and estimation of GGD parameters. Simulations show that heuristic approaches provide quite accurate estimates. In most of the cases, DE has better performance than other heuristics in terms of bias values of parameter estimations. Besides, the usefulness of an alternative parameter estimation method for GGD using the heuristic optimization approach is illustrated with a real data set.

广义伽玛分布（GGD）是一种流行的分布，因为它非常灵活。由于GGD的密度函数结构，通过统计点估计技术估计GGD系列的参数是一项复杂的任务。换句话说，对于参数估计，GGD的最大化似然函数是有问题的情况。因此，可以使用替代方法来获得GGD参数的估计量。本文提出了一种利用遗传算法（GA），差分演化（DE），粒子群优化（PSO）和模拟退火（SA）等启发式优化方法的GGD参数估计方法。本文对用于最大化似然函数以进行参数估计的不同现代启发式优化方法进行了比较。本文还研究了启发式方法的性能和GGD参数的估计。仿真表明，启发式方法提供了相当准确的估计。在大多数情况下，就参数估计的偏差值而言，DE的性能优于其他启发式方法。此外，还使用实际数据集说明了使用启发式优化方法的GGD替代参数估计方法的有用性。