Timely alerts about hazardous air pollutants are crucial for public health.
However, existing forecasting models often overlook key factors like baseline
parameters and missing data, limiting their accuracy. This study introduces a
hybrid approach to address these issues, focusing on forecasting hourly PM2.5
concentrations using Support Vector Regression (SVR). Meta-heuristic
algorithms, Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO),
optimize SVR Hyper-parameters "C" and "Gamma" to enhance prediction accuracy.
Evaluation metrics include R-squared (R2), Root Mean Square Error (RMSE), and
Mean Absolute Error (MAE). Results show significant improvements with PSO-SVR
(R2: 0.9401, RMSE: 0.2390, MAE: 0.1368) and GWO-SVR (R2: 0.9408, RMSE: 0.2376,
MAE: 0.1373), indicating robust and accurate models suitable for similar
research applications.

使用支持向量回归（SVR）方法，结合灰狼优化（GWO）和粒子群优化（PSO）算法来预测每小时 PM2.5 浓度，得到了可靠和准确的模型，适用于类似的研究应用。

利用混合元启发式和机器学习模型优化 PM2.5 预测准确性

Optimizing PM2.5 Forecasting Accuracy with Hybrid Meta-Heuristic and  Machine Learning Models

With the development of science and technology and the need for
Multi-Criteria Decision-Making (MCDM), the optimization problem to be solved
becomes extremely complex. The theoretically accurate and optimal solutions are
often difficult to obtain. Therefore, meta-heuristic algorithms based on
multi-point search have received extensive attention. Aiming at these problems,
the design strategy of hybrid flower pollination algorithm for Virtual Network
Embedding (VNE) problem is discussed. Combining the advantages of the Genetic
Algorithm (GA) and FPA, the algorithm is optimized for the characteristics of
discrete optimization problems. The cross operation is used to replace the
cross-pollination operation to complete the global search and replace the
mutation operation with self-pollination operation to enhance the ability of
local search. Moreover, a life cycle mechanism is introduced as a complement to
the traditional fitness-based selection strategy to avoid premature
convergence. A chaotic optimization strategy is introduced to replace the
random sequence-guided crossover process to strengthen the global search
capability and reduce the probability of producing invalid individuals.

讨论了基于混合花粉授粉算法的虚拟网络嵌入问题的设计策略，结合遗传算法和 FPA 算法的优点，采用交叉操作完成全局搜索、采用自交配操作增强局部搜索、引入生命周期机制和混沌优化策略以避免过早收敛。