Clustering is an important data analysis technique, which has been applied to many practical scenarios. However, many partitioning based clustering algorithms are sensitive to the initial state of cluster centroids, may get trapped in a local optimum, and have poor robustness. In recent years, particle swarm optimization (PSO) has been regarded as an effective solution to the problem. However, it has the possibility of converging to a local optimum, especially when solving complex problems. In this paper, we propose a hybrid PSO-K-means algorithm, which uses the Gaussian estimation of distribution method (GEDM) to assist PSO in updating the population information and adopts Lévy flight to escape from the local optimum. The proposed algorithm is named a GEDM and Lévy flight based PSO-K-means (GLPSOK) clustering algorithm. Firstly, during initialization, a few particles are initialized using the cluster centroids generated by K-means, while other particles are randomly initialized in the search space. Secondly, GEDM and PSO are selected with different probability to update the population information at different optimization stages. Thirdly, Lévy flight is adopted to help the search escape from the local optimum. Finally, the greedy strategy is carried out to select the promising particles from the parents and the newly generated candidates. Experimental results on both synthetic data sets and real-world data sets show that the proposed algorithm can produce better clustering results and is more robust than existing classic or state-of-the-art clustering algorithms.

This study explores the integration of the JADE (Java Agent Development Framework) platform with deep reinforcement learning (DRL) to enhance governmental interoperability and optimize administrative workflows in municipal settings. The proposed approach combines the JADE’s robust multi-agent system (MAS) capabilities with the adaptive decision-making power of DRL to address prevalent challenges faced by government agencies, such as fragmented operations, incompatible data formats, and rigid communication protocols. By enabling seamless communication between agents across departments such as the Treasury, the Event Management department, and the Public Safety department, the hybrid system fosters real-time collaboration and supports efficient, data-driven decision making. Agents leverage historical and real-time data to adapt to environmental changes and make optimized decisions that align with overarching governmental objectives, such as resource allocation and emergency response. The result is a system capable of managing intricate administrative duties using structured agent communication and the integration of DRL-driven learning models, improving governmental interoperability. Key performance indicators highlight the system’s effectiveness, achieving a task completion rate of 95%, decision accuracy of 96%, and a communication latency of just 120 ms. Additionally, the framework’s flexibility ensures seamless scalability, accommodating complex and large-scale tasks across multiple governmental units. This research presents a scalable, automated, and resilient framework for optimizing governmental processes, offering a pathway to more efficient, transparent, and adaptive public sector operations.