Heterogenous Wireless Sensor Networks (HWSNs) are cost-effective solutions to monitor the environment. These networks face limited battery as a critical challenge. Traditionally, energy-efficient routing algorithms ha...
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Heterogenous Wireless Sensor Networks (HWSNs) are cost-effective solutions to monitor the environment. These networks face limited battery as a critical challenge. Traditionally, energy-efficient routing algorithms have been the primary solution. While Artificial Intelligence (AI)-based approaches, particularly metaheuristics, show promise, current methods suffer from a key limitation: problem formulation hinders their success. Existing approaches rely on the array data structure for tree construction, leading to inefficiency. This paper proposes a novel geneticalgorithm (GA)-based approach for data collection in HWSNs. It introduces a simpler and more intuitive tree data structure specifically designed by GA, leading to improved performance. Additionally, the paper proposes population initialization schemes and GA operators customized for the problem. Finally, an advanced cost function is employed that distributes workloads based on hop count to the Base Station (BS), further optimizing energy consumption. Extensive simulations demonstrate the superiority of the proposed algorithm in data gathering. It increases the number of received bits to the BS by an average of 6.6%, while also almost significantly improves the network lifetime and the number of alive nodes.
Existing studies on saving energy consumption of train movement (ECTM) have focused mainly on optimizing train operation and service timetable. However, the ECTM is also significantly affected by metro vertical alignm...
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Existing studies on saving energy consumption of train movement (ECTM) have focused mainly on optimizing train operation and service timetable. However, the ECTM is also significantly affected by metro vertical alignment. This paper presents a mathematical model for optimizing the vertical alignment between any two adjacent underground metro stations, with the objective of minimizing the total ECTM in both train-running directions. The model takes into account train operation with variable speed limits and gradients in the calcu-lations of the ECTM, and a number of constraints involving local geographical conditions and design criteria set by the Code for Design of Metro in China. To solve the proposed model, a customized genetic algorithm (GA) with an indirect coding method is developed. Case studies on a real-world metro line show that the vertical alignments optimized by the model outperform that designed by experienced consultants in the ECTM savings;and the average energy saving rate on the total ECTM in a train's round trip exceeds 5%. In addition, the principles of designing metro vertical alignment with particular consideration of saving ECTM are summarized, which can be of great reference value to future vertical alignment design.
To improve the survivability of multiple ground vehicles when they are maneuvering under the reconnaissance of satellites, a survivability planning modeling method and a cooperative task area allocation method based o...
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ISBN:
(数字)9781510623002
ISBN:
(纸本)9781510623002
To improve the survivability of multiple ground vehicles when they are maneuvering under the reconnaissance of satellites, a survivability planning modeling method and a cooperative task area allocation method based on geneticalgorithm were proposed. Firstly, the problem of task area allocation for multiple ground vehicles is depicted and a framework to solve the problem is established. Then satellite reconnaissance model and cooperative task area allocation model are established. And a customized genetic algorithm is adopted to perform cooperative task area allocation to minimize the total mission time and the exposure time under the reconnaissance of satellites. In addition, a survivability assessment framework is established to evaluate the survival status of ground vehicles. Finally, simulation results show that the proposed method can improve the overall survivability of multiple ground vehicles effectively.
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