Enhanced whale optimization algorithms with source proximity indicators: Locating gaseous pollutants with time-varying release rates in weak airflow indoors

IF 12 1区 工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY Sustainable Cities and Society Pub Date : 2025-02-01 Epub Date: 2024-12-27 DOI:10.1016/j.scs.2024.106112
Jiamin Qiu , Hongyi Mao , Yaohua Jiang , Boyuan Zhang , Hao Cai
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Abstract

This study enhances the localization of stationary pollutant sources with time-varying release rates in indoor environments with weak airflow, addressing limitations of previous methods that were only effective for constantly released sources and dependent on concentration gradients. We refined the traditional whale optimization algorithm (WOA), based on mean concentration, by incorporating three novel source proximity indicators (SPIs): Bout, introduced by other researchers, and our newly developed modified proximity indicator (MPI) and source confidence (SC). These enhancements resulted in the development of three advanced methods: WOA_Bout, WOA_MPI, and WOA_SC. Using a custom-built multi-robot system, we conducted a two-stage experimental framework involving 120 trials across 8 scenarios to ensure statistical reliability. Our results demonstrate significant improvements in source localization, with WOA_SC achieving an impressive 90 % success rate, surpassing WOA_Bout at 83 %, WOA_MPI at 77 %, and significantly outperforming the traditional WOA at 60 %. Notably, in complex periodic source scenarios, WOA_SC maintained an 87 % success rate compared to WOA’s 40 %, demonstrating enhanced adaptability to variations in source release rates. This research underscores the effectiveness of integrating SPIs to improve localization strategies in indoor environments characterized by weak airflow.
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增强鲸鱼优化算法与源接近指标:定位气体污染物与时间变化的释放率在室内弱气流
本研究增强了对弱气流室内环境中随时间变化释放速率的固定污染源的定位,解决了以往方法仅对持续释放源有效且依赖浓度梯度的局限性。我们改进了传统的基于平均浓度的鲸鱼优化算法(WOA),加入了三个新的源接近指标(spi):其他研究人员引入的Bout,以及我们新开发的改进的接近指标(MPI)和源置信度(SC)。这些增强导致了三种高级方法的开发:WOA_Bout、WOA_MPI和WOA_SC。使用定制的多机器人系统,我们进行了两阶段的实验框架,涉及8种场景的120次试验,以确保统计可靠性。我们的结果表明,WOA_SC在源定位方面有了显著的改进,达到了令人印象深刻的90%的成功率,超过了WOA_Bout(83%)和WOA_MPI(77%),并显著优于传统WOA(60%)。值得注意的是,在复杂的周期性源场景中,WOA_SC保持了87%的成功率,而WOA的成功率为40%,这表明对源释放率变化的适应性增强了。本研究强调了在弱气流的室内环境中整合spi来改善定位策略的有效性。
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来源期刊
Sustainable Cities and Society
Sustainable Cities and Society Social Sciences-Geography, Planning and Development
CiteScore
22.00
自引率
13.70%
发文量
810
审稿时长
27 days
期刊介绍: Sustainable Cities and Society (SCS) is an international journal that focuses on fundamental and applied research to promote environmentally sustainable and socially resilient cities. The journal welcomes cross-cutting, multi-disciplinary research in various areas, including: 1. Smart cities and resilient environments; 2. Alternative/clean energy sources, energy distribution, distributed energy generation, and energy demand reduction/management; 3. Monitoring and improving air quality in built environment and cities (e.g., healthy built environment and air quality management); 4. Energy efficient, low/zero carbon, and green buildings/communities; 5. Climate change mitigation and adaptation in urban environments; 6. Green infrastructure and BMPs; 7. Environmental Footprint accounting and management; 8. Urban agriculture and forestry; 9. ICT, smart grid and intelligent infrastructure; 10. Urban design/planning, regulations, legislation, certification, economics, and policy; 11. Social aspects, impacts and resiliency of cities; 12. Behavior monitoring, analysis and change within urban communities; 13. Health monitoring and improvement; 14. Nexus issues related to sustainable cities and societies; 15. Smart city governance; 16. Decision Support Systems for trade-off and uncertainty analysis for improved management of cities and society; 17. Big data, machine learning, and artificial intelligence applications and case studies; 18. Critical infrastructure protection, including security, privacy, forensics, and reliability issues of cyber-physical systems. 19. Water footprint reduction and urban water distribution, harvesting, treatment, reuse and management; 20. Waste reduction and recycling; 21. Wastewater collection, treatment and recycling; 22. Smart, clean and healthy transportation systems and infrastructure;
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