The coverage of underwater sensor network (UWSN) is a basic problem of underwater wireless sensor network, which is related to the integrity and accuracy of underwater sensor network data collection for target monitor...
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ISBN:
(纸本)9781665440899
The coverage of underwater sensor network (UWSN) is a basic problem of underwater wireless sensor network, which is related to the integrity and accuracy of underwater sensor network data collection for target monitoring area. In addition, the working environment of underwater sensor nodes is often harsh and the influencing factors are complex, which is not convenient for frequent battery replacement. Therefore, optimizing the coverage of underwater sensor networks can effectively improve the monitoring quality and lifetime of UWSN. Aiming at the shortcomings of previous coverage optimization algorithms, such as easy to fall into the local optimal solution, complex parameters and slow convergence speed, this paper proposes a simulated annealing particle swarm optimization algorithm (sapso), which improves the particle swarm optimization algorithm (PSO) by introducing simulated annealing algorithm, so that the algorithm can maintain a faster convergence speed and overcome the shortcomings of PSO algorithm easy to fall into the local optimal solution, so as to improve the coverage of underwater sensor networks, It also makes the distribution of nodes more uniform and reduces the network energy consumption. Simulation results show that SAP SO algorithm can effectively improve the coverage rate of target monitoring area, reduce the redundancy of node coverage and reduce the network energy consumption.
The coverage of underwater sensor network（UWSN） is a basic problem of underwater wireless sensor network,which is related to the integrity and accuracy of underwater sensor network data collection for target monitor...
详细信息
The coverage of underwater sensor network（UWSN） is a basic problem of underwater wireless sensor network,which is related to the integrity and accuracy of underwater sensor network data collection for target monitoring *** addition,the working environment of underwater sensor nodes is often harsh and the influencing factors are complex,which is not convenient for frequent battery ***,optimizing the coverage of underwater sensor networks can effectively improve the monitoring quality and lifetime of *** at the shortcomings of previous coverage optimization algorithms,such as easy to fall into the local optimal solution,complex parameters and slow convergence speed,this paper proposes a simulated annealing particle swarm optimization algorithm（sapso）,which improves the particle swarm optimization algorithm（PSO） by introducing simulated annealing algorithm,so that the algorithm can maintain a faster convergence speed and overcome the shortcomings of PSO algorithm easy to fall into the local optimal solution,so as to improve the coverage of underwater sensor networks,It also makes the distribution of nodes more uniform and reduces the network energy *** results show that sapso algorithm can effectively improve the coverage rate of target monitoring area,reduce the redundancy of node coverage and reduce the network energy consumption.
Optimal reservoir operation is an important measure for ensuring flood-control safety and reducing disaster losses. The standard particle swarm optimization (PSO) algorithm can find the optimal solution of the problem...
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Optimal reservoir operation is an important measure for ensuring flood-control safety and reducing disaster losses. The standard particle swarm optimization (PSO) algorithm can find the optimal solution of the problem by updating its position and speed, but it is easy to fall into a local optimum. In order to prevent the problem of precocious convergence, a novel simulated annealing particle swarm optimization (sapso) algorithm was proposed in this study, in which the Boltzmann equation from the simulated annealing algorithm was incorporated into the iterative process of the PSO algorithm. Within the maximum flood peak reduction criterion, the sapso algorithm was used into two floods in the Tianzhuang-Bashan cascade reservoir system. The results shown that: (1) There are lower maximum outflows. The maximum outflows of Tianzhuang reservoir using sapso algorithm decreased by 9.3% and 8.6%, respectively, compared with the measured values, and those of Bashan reservoir decreased by 18.5% and 13.5%, respectively;(2) there are also lower maximum water levels. The maximum water levels of Tianzhuang reservoir were 0.39 m and 0.45 m lower than the measured values, respectively, and those of Bashan reservoir were 0.06 m and 0.46 m lower, respectively;and (3) from the convergence processes, the sapso algorithm reduced the convergence speed in the early stage of convergence and provided a superior objective function value than PSO algorithm. At the same time, by comparing with GA algorithm, the performance and applicability of sapso algorithm in flood operation are discussed further. Thus, the optimal operation model and sapso algorithm proposed in this study provide a new approach to realizing the optimal flood-control operation of cascade reservoir systems.
In this paper, in the so-called centralized charging and unified distribution mode, a two-stage optimization model is proposed for capacity planning and ordered discharging strategies of centralized charging stations ...
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In this paper, in the so-called centralized charging and unified distribution mode, a two-stage optimization model is proposed for capacity planning and ordered discharging strategies of centralized charging stations considering the peak-shaving effects. Firstly, the operating states of battery pack are analyzed at each moment by combining with distribution modes, then the first stage planning is processed, that is, by taking the numbers of battery pack and generators as the control variables, the mathematical model aiming at minimizing the construction cost of centralized charging stations can be solved by Simulated Annealing Particle Swarm Optimization (sapso). Next, in the second stage planning, in order to maximize the peaking effect of the centralized charging station, the cost function aiming at minimizing load variance is proposed, and the discharge power of each time can be obtained by using yalmip toolbox. Finally, simulation results are provided to verify the effectiveness and usefulness of the proposed optimization model.
In the free-floating mode, there is intense dynamic coupling existing between the space manipulator and the base, and the base attitude may change while performing a motion with its manipulator. Therefore, it is neces...
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In the free-floating mode, there is intense dynamic coupling existing between the space manipulator and the base, and the base attitude may change while performing a motion with its manipulator. Therefore, it is necessary to reduce the interference that resulted from the manipulator movement. For planning trajectories of the space manipulator with 7 degrees of freedom (7-DOF), simulated annealing particle swarm optimization (sapso) algorithm is presented in the paper. Firstly, kinematics equations are setup. Secondly, the joint functions are parameterized by sinusoidal functions, and the objective function is defined according to the motion constraints of manipulator and accuracy requirements of the base attitude. Finally, sapso algorithm is used to search the optimal trajectory. The simulation results verify the proposed method.
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