## 1 Introduction  
Optimizing gas leak detection sensor placement in urban gas networks is crucial for both public safety and operational cost management [1, 2]. A balance must be struck between cost-effectiveness and safety, as too many sensors increase costs, while too few may miss leaks, posing safety risks [3, 4]. Risk analysis using system clustering can help identify and evaluate potential risks by classifying nodes in the pipeline network [5]. This unsupervised method analyzes features such as historical failure records, geographic location, environment, and pipeline materials without predefined categories [6, 7]. However, system clustering is sensitive to data quality and quantity, and noise or outliers can affect clustering accuracy [8, 9]. To implement this, data like historical accident records, real-time monitoring, and environmental factors are collected and processed. Clustering algorithms such as K-meansor hierarchical clustering are used to classify nodes by risk, identifying high-risk and low-risk areas for targeted safety measures [10; 11]. The choice of algorithm and parameter settings can significantly affect results [12]. Further methods, like the AHP-TOPSIS model for risk assessment [13] can integrate with clustering, though subjective judgment in these models can introduce uncertainties [14]. Ant colony algorithms, which simulate ant foraging behavior, are increasingly used to optimize sensor placement by strategically positioning sensors around high-risk nodes for real-time monitoring [15; 16; 17]. However, these algorithms can face challenges such as high computational complexity and slow convergence in large-scale problems [18]. In summary, while system clustering improves risk analysis and supports safety measures, limitations like data quality, algorithm selection, and subjective judgment remain. Future research could integrate machine learning and data mining to enhance urban gas network safety and reliability [19; 20]. In sensor placement optimization, the ant colony optimization (ACO) algorithm is praised for its global search capabilities and adaptability, effectively solving complex combinatorial problems [21]. However, it has practical limitations. [PERSON], [PERSON], and [PERSON] [22] studied optimal placement of piezoelectric sensors on smart structures, focusing on maximizing control ability and minimizing effort. Their study overlooked dynamic changes in sensor placement, which can reduce sensor effectiveness due to external factors such as environmental conditions (e.g., soil moisture, temperature variations), physical obstructions (e.g., new buildings or constructions), and changes in gas diffusion patterns over time. These factors can lead to sensor misalignment with the gas leakage sources or reduced sensitivity due to interference, ultimately affecting the accuracy and reliability of the monitoring system. [PERSON], [PERSON], and [PERSON] [23] introduced a sensor deployment approach based on the attribute hierarchy model, considering system heterogeneity. While innovative, it assumes an idealized system and neglects sensor failures and errors. [PERSON], [PERSON], [PERSON], and [PERSON] [3] enhanced ACO with visual feedback and behavioral memory to improve search capabilities. Although it theoretically improves search quality, its complexity and sensitivity to parameter settings can lead to unstable performance. [PERSON] [24] introduced a low-energy secure routing protocol using a multi-objective ant colony optimization algorithm, emphasizing energy efficiency in wireless sensor networks. While it optimizes energy use and extends network lifespan, it faces limitations in dynamic environments due to potential local optima issues. [PERSON], [PERSON], [PERSON], and [PERSON] [25] combined ant colony algorithms with neural networks for sensor compensation, excelling in accuracy but potentially suffering from data quality dependency, particularly in sparse or noisy data.