Qin Huaiyu, Ding Wei, Xu Lei, Ruan Chenzhi
School of Electrical and Information Engineering, Jiangsu University of Science and Technology, Zhenjiang 212000, China.
The Key Laboratory for Agricultural Machinery Intelligent Control and Manufacturing of Fujian Education Institution, Wuyishan 354330, China.
Sensors (Basel). 2024 Sep 29;24(19):6316. doi: 10.3390/s24196316.
In order to express the energy flow, motion flow, and control flow in wireless rechargeable sensor networks accurately and intuitively, and to maximize the charging benefit of MVs (mobile vehicles), a type of MTS-HACO (Mobile Transition Sequence Hybrid Ant Colony Optimization) is proposed. Firstly, node places are grouped according to the firing time of node's energy consumption transition to ensure that in each time slot, MV places only enable charging transitions for the node places with lower remaining lifetimes. Then, the FSOMCT (Firing Sequence Optimization of Mobile Charging Transition) problem is formulated under the constraints of MV places capacity, travelling arc weight, charging arc weight, and so on. The elite strategy and the Max-Min Ant Colony system are further introduced to improve the ant colony algorithm, while the improved FWA (fireworks algorithm) optimizes the path constructed by each ant. Finally, the optimal mobile charging transition firing sequence and charging times are obtained, ensuring that MVs have sufficient energy to return to the base station. Simulation results indicate that, compared with the periodic algorithm and the PE-FWA algorithm, the proposed method can improve charging benefit by approximately 48.7% and 26.3%, respectively.
为了准确直观地表达无线可充电传感器网络中的能量流、运动流和控制流，并最大化移动车辆（MV）的充电效益，提出了一种移动过渡序列混合蚁群优化算法（MTS-HACO）。首先，根据节点能量消耗转变的触发时间对节点位置进行分组，以确保在每个时隙中，移动车辆位置仅对剩余寿命较短的节点位置启用充电转变。然后，在移动车辆位置容量、行进弧权重、充电弧权重等约束条件下，构建移动充电转变触发序列优化（FSOMCT）问题。进一步引入精英策略和最大最小蚁群系统来改进蚁群算法，同时改进的烟花算法（FWA）优化每只蚂蚁构建的路径。最后，获得最优的移动充电转变触发序列和充电次数，确保移动车辆有足够的能量返回基站。仿真结果表明，与周期算法和PE-FWA算法相比，该方法可分别将充电效益提高约48.7%和26.3%。
