Meta-heuristic algorithms have found their place in optimization problems. Henry gas solubility optimization (HGSO) is one of the newest population-based algorithms. This algorithm is inspired by Henry's law of physics. To evaluate the performance of a new algorithm, it must be used in various problems. On the other hand, the optimization of the proportional–integral–derivative (PID) gains for load-following of a nuclear power plant (NPP) is a good challenge to assess the performance of HGSO. Accordingly, the power control of a pressurized water reactor (PWR) is targeted, based on the point kinetics model with six groups of delayed-neutron precursors. In any optimization problem based on meta-heuristic algorithms, an efficient objective function is required. Therefore, the integral of the time-weighted square error (ITSE) performance index is utilized as the objective (cost) function of HGSO, which is constrained by a stability criterion in steady-state operations. A Lyapunov approach guarantees this stability. The results show that this method provides superior results compared to an empirically tuned PID controller with the least error. It also achieves good accuracy compared to an established GA-tuned PID controller.

元启发式算法已经在优化问题中找到了自己的位置。亨利气体溶解度优化 (HGSO) 是最新的基于群体的算法之一。该算法受到亨利物理定律的启发。为了评估一种新算法的性能，它必须用于各种问题。另一方面，优化核电厂 (NPP) 负载跟踪的比例-积分-微分 (PID) 增益是评估 HGSO 性能的一个很好的挑战。因此，基于具有六组延迟中子前体的点动力学模型，有针对性地控制压水堆（PWR）的功率。在任何基于元启发式算法的优化问题中，都需要一个有效的目标函数。所以，时间加权平方误差 (ITSE) 性能指标的积分被用作 HGSO 的目标（成本）函数，该函数受稳态操作中的稳定性标准约束。Lyapunov 方法保证了这种稳定性。结果表明，与经验调整的 PID 控制器相比，该方法提供了更好的结果，并且误差最小。与已建立的 GA 调谐 PID 控制器相比，它还实现了良好的精度。