scholarly journals A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Wei-Min Zheng ◽  
Ning Liu ◽  
Qing-Wei Chai ◽  
Shu-Chuan Chu
Keyword(s):  
Heuristic Algorithms ◽  
Rapid Development ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Mobile Sensor ◽  
Sensor Localization ◽  
Node Localization ◽  
Monte Carlo Localization ◽  
Specific Position ◽  
Swarm Algorithm

The mobile sensor network can sense and collect the data information of the monitored object in real time in the monitoring area. However, the collected information is meaningful only if the location of the node is known. This paper mainly optimizes the Monte Carlo Localization (MCL) in mobile sensor positioning technology. In recent years, the rapid development of heuristic algorithms has provided solutions to many complex problems. This paper combines the compact strategy into the adaptive particle swarm algorithm and proposes a compact adaptive particle swarm algorithm (cAPSO). The compact strategy replaces the specific position of each particle by the distribution probability of the particle swarm, which greatly reduces the memory usage. The performance of cAPSO is tested on 28 test functions of CEC2013, and compared with some existing heuristic algorithms, it proves that cAPSO has a better performance. At the same time, cAPSO is applied to MCL technology to improve the accuracy of node localization, and compared with other heuristic algorithms in the accuracy of MCL, the results show that cAPSO has a better performance.

scholarly journals Modified particle swarm algorithm for the optimal water allocation of reservoir

2020 ◽  
Vol 20 (7) ◽  
pp. 2875-2883
Author(s):  
Zhihao Gong ◽  
Jilin Cheng ◽  
Yi Gong ◽  
Liang Wang ◽  
Cong Wei
Keyword(s):  
Heuristic Algorithms ◽  
Direct Method ◽  
Particle Swarm ◽  
Optimal Operation ◽  
Water Levels ◽  
Particle Swarm Algorithm ◽  
Probability Of Exceedance ◽  
Modified Method ◽  
Operation Rule ◽  
Swarm Algorithm

Abstract At present, meta-heuristic algorithms are the most popular methods for the optimization of the operations of reservoirs. In order to avoid inappropriate solutions, i.e. spills occurring when the reservoir is not full, a modified method is proposed that can adjust the trajectories of the particles, using the particle swarm algorithm, according to the operation rule of the reservoir. The method was tested in a case study, and was compared to two commonly used methods for generating particle position vectors. These included the direct method, which uses water supply and water spills as the iteration variables, and the indirect method, which uses water storages (water levels) as the iteration variables. The results showed that the three methods could achieve similar solutions at the 75% probability of exceedance. There was no difference in the convergence speeds or the final objective function values of the three models. However, at the 50% probability of exceedance, the modified method produced results that followed the operation rule of the reservoir, whereas the other two methods could lead to inappropriate water spills. This new method may provide a reference for other meta-heuristic algorithms in models of the optimal operation of reservoirs.

scholarly journals Research on the Effect of DPSO in Team Selection Optimization under the Background of Big Data

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Qian Zhao ◽  
Lian-ying Zhang
Keyword(s):  
Big Data ◽  
Rapid Development ◽  
Particle Swarm ◽  
Practical Significance ◽  
Particle Swarm Algorithm ◽  
Enterprise Information ◽  
Strategic Objectives ◽  
Management Platform ◽  
Team Selection ◽  
Swarm Algorithm

Team selection optimization is the foundation of enterprise strategy realization; it is of great significance for maximizing the effectiveness of organizational decision-making. Thus, the study of team selection/team foundation has been a hot topic for a long time. With the rapid development of information technology, big data has become one of the significant technical means and played a key role in many researches. It is a frontier of team selection study by the means of combining big data with team selection, which has the great practical significance. Taking strategic equilibrium matching and dynamic gain as association constraints and maximizing revenue as the optimization goal, the Hadoop enterprise information management platform is constructed to discover the external environment, organizational culture, and strategic objectives of the enterprise and to discover the potential of the customer. And in order to promote the renewal of production and cooperation mode, a team selection optimization model based on DPSO is built. The simulation experiment method is used to qualitatively analyze the main parameters of the particle swarm optimization in this paper. By comparing the iterative results of genetic algorithm, ordinary particle swarm algorithm, and discrete particle swarm algorithm, it is found that the DPSO algorithm is effective and preferred in the study of team selection with the background of big data.

Dynamic simulation of wind field in three-dimensional display space considering particle swarm algorithm fuzzy logic

2021 ◽  
pp. 1-6
Author(s):  
Honglei Xu ◽  
Linhuan Wang
Keyword(s):  
Fuzzy Logic ◽  
Dynamic Simulation ◽  
Wind Field ◽  
Three Dimensional ◽  
Measurement Data ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Three Dimensional Display ◽  
Swarm Algorithm ◽  
Synchronous Measurement

In order to improve the accuracy of dynamic detection of wind field in the three-dimensional display space, system software is carried out on the actual scene and corresponding airborne radar observation information data, and the particle swarm algorithm fuzzy logic algorithm is introduced into the wind field dynamic simulation process in three-dimensional display space, to analyze the error of the filtering result in detail, to process the hurricane Lily Doppler radar measurement data with the optimal adaptive filtering according to the error data. The three-dimensional wind field synchronous measurement data obtained by filtering was compared with three-dimensional wind field synchronous measurement data of the GPS dropsonde in this experiment, the sea surface wind field measurement data of the multi-band microwave radiometer, and the wind field data at aircraft altitude.

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