基于无线传感器感知模型,提出了一种粒子间距调整改进粒子群优化算法(Improved particle swarm optimization based on adjusting particle spacing,APS-PSO),利用APS-PSO算法优化WSN在目标区域内的部署,有效提高了WSN的覆盖率。首先,...基于无线传感器感知模型,提出了一种粒子间距调整改进粒子群优化算法(Improved particle swarm optimization based on adjusting particle spacing,APS-PSO),利用APS-PSO算法优化WSN在目标区域内的部署,有效提高了WSN的覆盖率。首先,针对粒子越界和粒子间发生重叠等多样性消失的问题,在迭代过程中引入粒子间距调整(APS)来增强粒子多样性,其次,通过为种群中粒子细化寻优方向,能够尽可能的重启早熟粒子。通过目标区域离散化以及传感器节点的特性定义目标函数,将其代入到APS-PSO中,从而找到较好的覆盖方案。通过Matlab仿真结果表明:该算法提高了传感器节点分布的均匀度,网络的覆盖率也得到了提高,而且也具有相对较好的稳定性。展开更多
A novel reactor that achieves rapid liquid–liquid mixing via free triple-impinging jets(FTIJs) is developed to improve mixing efficiency at unequal flow rates for liquid–liquid reactions. The flow characteristics of...A novel reactor that achieves rapid liquid–liquid mixing via free triple-impinging jets(FTIJs) is developed to improve mixing efficiency at unequal flow rates for liquid–liquid reactions. The flow characteristics of FTIJs were investigated using particle image velocimetry(PIV). The instantaneous and mean velocities data at different Reynolds numbers(Re) were analyzed to provide insights into the velocity distributions in FTIJs. The effect of jet spacing on the stagnation points, instantaneous velocity, mean velocity, profiles of the x- and ycomponents of mean velocity, and turbulent kinetic energy(TKE) distributions of FTIJs were investigated at Re = 4100 with a volumetric flow rate ratio of 0.5. The characteristics of the turbulent flows are similar for all jet spacings tested. Two stagnation points are observed, which are independent of jet spacing and are not located in the center of the flow field. However, velocity and TKE distributions are strongly dependent on the jet spacing.Decreasing jet spacing increases the expansion angle and the values of TKE, leading to strong turbulence, improving momentum transfer and mixing efficiency in FTIJs. The present study shows that optimization of the operating parameters is helpful for designing FTIJs.展开更多
To improve the convergence and distributivity of multi-objective particle swarm optimization,we propose a method for multi-objective particle swarm optimization by fusing multiple strategies(MOPSO-MS),which includes t...To improve the convergence and distributivity of multi-objective particle swarm optimization,we propose a method for multi-objective particle swarm optimization by fusing multiple strategies(MOPSO-MS),which includes three strategies.Firstly,the average crowding distance method is proposed,which takes into account the influence of individuals on the crowding distance and reduces the algorithm’s time complexity and computational cost,ensuring efficient external archive maintenance and improving the algorithm’s distribution.Secondly,the algorithm utilizes particle difference to guide adaptive inertia weights.In this way,the degree of disparity between a particle’s historical optimum and the population’s global optimum is used to determine the value of w.With different degrees of disparity,the size of w is adjusted nonlinearly,improving the algorithm’s convergence.Finally,the algorithm is designed to control the search direction by hierarchically selecting the globally optimal policy,which can avoid a single search direction and eliminate the lack of a random search direction,making the selection of the global optimal position more objective and comprehensive,and further improving the convergence of the algorithm.The MOPSO-MS is tested against seven other algorithms on the ZDT and DTLZ test functions,and the results show that the MOPSO-MS has significant advantages in terms of convergence and distributivity.展开更多
文摘基于无线传感器感知模型,提出了一种粒子间距调整改进粒子群优化算法(Improved particle swarm optimization based on adjusting particle spacing,APS-PSO),利用APS-PSO算法优化WSN在目标区域内的部署,有效提高了WSN的覆盖率。首先,针对粒子越界和粒子间发生重叠等多样性消失的问题,在迭代过程中引入粒子间距调整(APS)来增强粒子多样性,其次,通过为种群中粒子细化寻优方向,能够尽可能的重启早熟粒子。通过目标区域离散化以及传感器节点的特性定义目标函数,将其代入到APS-PSO中,从而找到较好的覆盖方案。通过Matlab仿真结果表明:该算法提高了传感器节点分布的均匀度,网络的覆盖率也得到了提高,而且也具有相对较好的稳定性。
基金Supported by the Graduate Innovation Foundation of Shanxi Province of China(2015BY44)
文摘A novel reactor that achieves rapid liquid–liquid mixing via free triple-impinging jets(FTIJs) is developed to improve mixing efficiency at unequal flow rates for liquid–liquid reactions. The flow characteristics of FTIJs were investigated using particle image velocimetry(PIV). The instantaneous and mean velocities data at different Reynolds numbers(Re) were analyzed to provide insights into the velocity distributions in FTIJs. The effect of jet spacing on the stagnation points, instantaneous velocity, mean velocity, profiles of the x- and ycomponents of mean velocity, and turbulent kinetic energy(TKE) distributions of FTIJs were investigated at Re = 4100 with a volumetric flow rate ratio of 0.5. The characteristics of the turbulent flows are similar for all jet spacings tested. Two stagnation points are observed, which are independent of jet spacing and are not located in the center of the flow field. However, velocity and TKE distributions are strongly dependent on the jet spacing.Decreasing jet spacing increases the expansion angle and the values of TKE, leading to strong turbulence, improving momentum transfer and mixing efficiency in FTIJs. The present study shows that optimization of the operating parameters is helpful for designing FTIJs.
基金National Natural Science Foundation of China(No.61702006)Open Fund of Key laboratory of Anhui Higher Education Institutes(No.CS2021-ZD01)。
文摘To improve the convergence and distributivity of multi-objective particle swarm optimization,we propose a method for multi-objective particle swarm optimization by fusing multiple strategies(MOPSO-MS),which includes three strategies.Firstly,the average crowding distance method is proposed,which takes into account the influence of individuals on the crowding distance and reduces the algorithm’s time complexity and computational cost,ensuring efficient external archive maintenance and improving the algorithm’s distribution.Secondly,the algorithm utilizes particle difference to guide adaptive inertia weights.In this way,the degree of disparity between a particle’s historical optimum and the population’s global optimum is used to determine the value of w.With different degrees of disparity,the size of w is adjusted nonlinearly,improving the algorithm’s convergence.Finally,the algorithm is designed to control the search direction by hierarchically selecting the globally optimal policy,which can avoid a single search direction and eliminate the lack of a random search direction,making the selection of the global optimal position more objective and comprehensive,and further improving the convergence of the algorithm.The MOPSO-MS is tested against seven other algorithms on the ZDT and DTLZ test functions,and the results show that the MOPSO-MS has significant advantages in terms of convergence and distributivity.
