Evolutionary Computation and Artificial Intelligence: Building a Sustainable Future for Smart Cities

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Guest Editor
College of Mathematics and Computer Science, Zhejiang Normal University, Jinhua 321004, China
Interests: evolutionary computation; communication deployment; image processing; DNA computation
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Computer Science and Technology, Zhejiang Normal University, Jinhua, China
Interests: state estimation; fault detection; deep learning; variational inference

Special Issue Information

Dear Colleagues,

The rapid urbanization and increasing complexity of modern cities have created unprecedented challenges in resource management, infrastructure optimization, and environmental sustainability. As cities strive to become smarter and more sustainable, the integration of evolutionary computation and artificial intelligence (AI) offers transformative potential. Evolutionary computation, with its ability to solve complex optimization problems, and AI, with its capacity for data-driven decision-making, are poised to revolutionize urban planning, energy management, and environmental monitoring. This Special Issue, titled "Evolutionary Computation and Artificial Intelligence: Building a Sustainable Future for Smart Cities", aims to explore the synergies between these technologies and their applications in addressing the multifaceted challenges of smart city development.

This Special Issue seeks to compile state-of-the-art research that examines the role of evolutionary computation and AI in advancing smart city initiatives. We invite contributions that investigate the economic, technological, and social dimensions of these technologies, with a focus on their potential to enhance urban sustainability, efficiency, and resilience. By fostering interdisciplinary dialog, this issue aims to inform strategies for sustainable urban development and contribute to the broader discourse on the future of smart cities.

We invite submissions of empirical, theoretical, and review papers on topics that include, but are not limited to, the following:

  • Evolutionary Computation for Urban Optimization: Applications of evolutionary algorithms in optimizing urban infrastructure, resource allocation, and transportation systems;
  • AI-Driven Smart City Solutions: The role of AI in enhancing urban services, such as energy management, waste reduction, and public safety;
  • Integration of IoT and 6G Technologies: Exploring how IoT and 6G communication technologies can synergize with evolutionary computation and AI to enable real-time urban monitoring and decision-making;
  • Sustainable Energy Systems: The use of evolutionary computation and AI in optimizing renewable energy integration, energy storage, and grid management in smart cities;
  • Environmental Monitoring and Resilience: Applications of AI and evolutionary computation in predicting and mitigating environmental risks, such as air pollution and climate change impacts;
  • Policy and Governance for Smart Cities: Analyses of policy frameworks that support the adoption of evolutionary computation and AI in urban planning and management;
  • Social and Economic Impacts: Investigating how AI and evolutionary computation can promote social equity, economic growth, and community well-being in smart cities;
  • Case Studies and Best Practices: Lessons learned from the implementation of evolutionary computation and AI in specific smart city projects worldwide;
  • Ethical and Privacy Considerations: Addressing the ethical challenges and privacy concerns associated with the use of AI and data-driven technologies in urban environments.

We look forward to your contributions and believe that this Special Issue will serve as a valuable platform for advancing research, fostering innovation, and informing policy in the pursuit of sustainable and intelligent urban futures.

Prof. Dr. Changjun Zhou
Dr. Zhichao Pan
Guest Editors

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Keywords

  • Internet of Things (IoT)
  • 6G communication
  • evolutionary computation
  • real-time data stream processing and optimization
  • edge computing optimization
  • AI-driven analytics
  • distributed computing and resource scheduling
  • network slicing technology
  • privacy protection mechanisms
  • heterogeneous network collaboration

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Published Papers (4 papers)

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Research

24 pages, 2532 KB  
Article
Improved Particle Swarm Optimization Based on Fuzzy Controller Fusion of Multiple Strategies for Multi-Robot Path Planning
by Jialing Hu, Yanqi Zheng, Siwei Wang and Changjun Zhou
Big Data Cogn. Comput. 2025, 9(9), 229; https://doi.org/10.3390/bdcc9090229 - 2 Sep 2025
Viewed by 548
Abstract
Robots play a crucial role in experimental smart cities and are ubiquitous in daily life, especially in complex environments where multiple robots are often needed to solve problems collaboratively. Researchers have found that the swarm intelligence optimization algorithm has a better performance in [...] Read more.
Robots play a crucial role in experimental smart cities and are ubiquitous in daily life, especially in complex environments where multiple robots are often needed to solve problems collaboratively. Researchers have found that the swarm intelligence optimization algorithm has a better performance in planning robot paths, but the traditional swarm intelligence algorithm cannot be targeted to solve the robot path planning problem in difficult problem. Therefore, this paper aims to introduce a fuzzy controller, mutation factor, exponential noise, and other strategies on the basis of particle swarm optimization to solve this problem. By judging the moving speed of different particles at different periods of the algorithm, the individual learning factor and social learning factor of the particles are obtained by fuzzy controller, and using the leader particle and random particle, designing a new dynamic balance of mutation factor, with the iterative process of the adaptation value of continuous non-updating counter and continuous updating counter to control the proportion of the elite individuals and random individuals. Finally, using exponential noise to update the matrix of the population every 50 iterations is a way to balance the local search ability and global exploration ability of the algorithm. In order to test the proposed algorithm, the main method of this paper is simulated on simple scenarios, complex scenarios, and random maps consisting of different numbers of static obstacles and dynamic obstacles, and the algorithm proposed in this paper is compared with eight other algorithms. The average path deviation error of the planned paths is smaller; the average distance of untraveled target is shorter; the number of steps of the robot movements is smaller, and the path is shorter, which is superior to the other eight algorithms. This superiority in solving multi-robot cooperative path planning has good practicality in many fields such as logistics and distribution, industrial automation operation, and so on. Full article
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20 pages, 1357 KB  
Article
FedPLDSE: Submodel Extraction for Federated Learning in Heterogeneous Smart City Devices
by Xiaochi Hou, Zhigang Wang, Xinhao Wang and Junfeng Zhao
Big Data Cogn. Comput. 2025, 9(9), 226; https://doi.org/10.3390/bdcc9090226 - 30 Aug 2025
Viewed by 485
Abstract
Federated learning enables collaborative model training across distributed devices while preserving data privacy. However, in real-world environments such as smart cities, heterogeneous and resource-constrained edge devices often render existing methods impractical. Low-power sensors and cameras struggle to complete full-model training, while high-performance devices [...] Read more.
Federated learning enables collaborative model training across distributed devices while preserving data privacy. However, in real-world environments such as smart cities, heterogeneous and resource-constrained edge devices often render existing methods impractical. Low-power sensors and cameras struggle to complete full-model training, while high-performance devices remain idly waiting for others. Knowledge distillation approaches rely on public datasets that are rarely available or poorly aligned with urban data, which limits their effectiveness in deployment. These limitations lead to inefficiencies, unstable convergence, and poor adaptability in diverse urban networks. Partial training alleviates some challenges by allowing clients to train submodels tailored to their capacity, but existing methods still incur high computational costs for identifying important parameters and suffer from uneven parameter updates, reducing model effectiveness. To address these challenges, we propose Parameter-Level Dynamic Submodel Extraction (PLDSE), a lightweight and adaptive framework for federated learning. PLDSE estimates parameter importance using gradient-based scores on a server-side validation set, reducing overhead while accurately identifying critical parameters. In addition, it integrates a rolling scheduling mechanism to rotate unselected parameters, ensuring full coverage and consistent model updates. Experiments on CIFAR-10, CIFAR-100, and Fashion-MNIST demonstrate superior accuracy and faster convergence, with PLDSE achieving 62.82% on CIFAR-100 under low heterogeneity and 61.51% under high heterogeneity, outperforming prior methods. Full article
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22 pages, 2132 KB  
Article
Ontology Matching Method Based on Deep Learning and Syntax
by Jiawei Lu and Changfeng Yan
Big Data Cogn. Comput. 2025, 9(8), 208; https://doi.org/10.3390/bdcc9080208 - 14 Aug 2025
Viewed by 483
Abstract
Ontology technology addresses data heterogeneity challenges in Internet of Everything (IoE) systems enabled by Cyber Twin and 6G, yet the subjective nature of ontology engineering often leads to differing definitions of the same concept across ontologies, resulting in ontology heterogeneity. To solve this [...] Read more.
Ontology technology addresses data heterogeneity challenges in Internet of Everything (IoE) systems enabled by Cyber Twin and 6G, yet the subjective nature of ontology engineering often leads to differing definitions of the same concept across ontologies, resulting in ontology heterogeneity. To solve this problem, this study introduces a hybrid ontology matching method that integrates a Recurrent Neural Network (RNN) with syntax-based analysis. The method first extracts representative entities by leveraging in-degree and out-degree information from ontological tree structures, which reduces training noise and improves model generalization. Next, a matching framework combining RNN and N-gram is designed: the RNN captures medium-distance dependencies and complex sequential patterns, supporting the dynamic optimization of embedding parameters and semantic feature extraction; the N-gram module further captures local information and relationships between adjacent characters, improving the coverage of matched entities. The experiments were conducted on the OAEI benchmark dataset, where the proposed method was compared with representative baseline methods from OAEI as well as a Transformer-based method. The results demonstrate that the proposed method achieved an 18.18% improvement in F-measure over the best-performing baseline. This improvement was statistically significant, as validated by the Friedman and Holm tests. Moreover, the proposed method achieves the shortest runtime among all the compared methods. Compared to other RNN-based hybrid frameworks that adopt classical structure-based and semantics-based similarity measures, the proposed method further improved the F-measure by 18.46%. Furthermore, a comparison of time and space complexity with the standalone RNN model and its variants demonstrated that the proposed method achieved high performance while maintaining favorable computational efficiency. These findings confirm the effectiveness and efficiency of the method in addressing ontology heterogeneity in complex IoE environments. Full article
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18 pages, 19551 KB  
Article
FAD-Net: Automated Framework for Steel Surface Defect Detection in Urban Infrastructure Health Monitoring
by Nian Wang, Yue Chen, Weiang Li, Liyang Zhang and Jinghong Tian
Big Data Cogn. Comput. 2025, 9(6), 158; https://doi.org/10.3390/bdcc9060158 - 13 Jun 2025
Viewed by 849
Abstract
Steel plays a fundamental role in modern smart city development, where its surface structural integrity is decisive for operational safety and long-term sustainability. While deep learning approaches show promise, their effectiveness remains limited by inadequate receptive field adaptability, suboptimal feature fusion strategies, and [...] Read more.
Steel plays a fundamental role in modern smart city development, where its surface structural integrity is decisive for operational safety and long-term sustainability. While deep learning approaches show promise, their effectiveness remains limited by inadequate receptive field adaptability, suboptimal feature fusion strategies, and insufficient sensitivity to small defects. To overcome these limitations, we propose FAD-Net, a deep learning framework specifically designed for surface defect detection in steel materials within urban infrastructure. The network incorporates three key innovations: The RFCAConv module, which leverages dynamic receptive field construction and coordinate attention mechanisms to enhance feature representation for defects with long-range spatial dependencies and low-contrast characteristics. The MSDFConv module, employing multi-scale dilated convolutions with optimized dilation rates to preserve fine details while expanding the receptive field. An Auxiliary Head that introduces hierarchical supervision to improve the detection of small-scale defects. Experiments on the GC10-DET dataset showed that FAD-Net achieved 5.0% higher mAP@0.5 than baseline models. Cross-dataset validation with NEU and RDD2022 further confirmed its robustness. These results demonstrate FAD-Net’s effectiveness for automated infrastructure health monitoring. Full article
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