Traffic Prediction Model Based on Improved Quantum Particle Swarm Algorithm in Wireless Network

Investment Portfolio ◽

Model Based ◽

In summary, firstly, a method for establishing a portfolio model is proposed based on the risk management theory of the financial market. Then, a prediction model for CVaR is established based on the convolutional neural network, and the improved particle swarm algorithm is employed to solve the model. The actual data analysis is implemented to prove the feasibility of CVaR prediction model based on deep learning and particle swarm optimization algorithm in financial market risk management. The test results show that the investment portfolio CVaR prediction model based on the convolutional neural network can obtain the optimal solution in the 18th generation at the fastest after using the improved particle swarm algorithm, which is more effective than the traditional algorithm. The CVaR prediction model of the investment portfolio based on the convolutional neural network facilitates the risk management of the financial market.

Microgrid Trading Game Model Based on Blockchain Technology and Optimized Particle Swarm Algorithm

IEEE Access ◽

10.1109/access.2020.3044980 ◽

2020 ◽

pp. 1-1

Author(s):

Binyao Liu ◽

Ming Wang ◽

Jiakai Men ◽

Dechang Yang

Keyword(s):

Particle Swarm ◽

Game Model ◽

Model Based ◽

Blockchain Technology ◽

A Kind of Adaptive Quantum Particle Swarm Algorithm Based on Phase Encoding

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.548.612 ◽

2012 ◽

Vol 548 ◽

pp. 612-616

Author(s):

Jun Hui Pan ◽

Hui Wang ◽

Pan Chi Li

Keyword(s):

Particle Swarm Optimization Algorithm ◽

Optimization Problems ◽

Quantum Particle ◽

Particle Swarm ◽

Quantum Bits ◽

Swarm Algorithm ◽

The Relationship ◽

Better Than ◽

Function Extremum

To improve the optimization performance of particle swarm, an adaptive quantum particle swarm optimization algorithm is proposed. In the algorithm, the spatial position of particles is described by the phase of quantum bits, and the position mutation of particles is achieved by Pauli-Z gates. An adaptive determination method of the global-factors is proposed by studying the relationship among inertia factors, self-factors and global-factors. The experimental results demonstrate that the proposed algorithm is much better than the standard particle swarm algorithm by solving the function extremum optimization problems.

2010 International Conference on Computer Application and System Modeling (ICCASM 2010) ◽

An optimized quantum particle swarm algorithm based on the D-dimensional hyper-chaotic discrete system equation

10.1109/iccasm.2010.5622858 ◽

2010 ◽

Author(s):

Li Yangjun ◽

Jin Yan Xia ◽

Wang Gao

Keyword(s):

Discrete System ◽

Quantum Particle ◽

Particle Swarm ◽

System Equation ◽

Capacity Configuration Optimization of Micro-grid System basedon Quantum Particle Swarm Algorithm

E3S Web of Conferences ◽

10.1051/e3sconf/202125201021 ◽

2021 ◽

Vol 252 ◽

pp. 01021

Author(s):

Zhang Yuqiong ◽

Chen Ziwei ◽

Shao Zhifang ◽

Zhao Qiang ◽

Han Chuyin

Keyword(s):

Power Generation ◽

Energy Storage ◽

Hydrogen Production ◽

Quantum Particle ◽

Particle Swarm ◽

Environmental Benefits ◽

Grid System ◽

Micro Grid ◽

The optimized configuration of wind/photovoltaic/storage micro-grid system capacity can realize multi-energy complementation and improve the stability and economy of grid-connected operation of power generation units. In this paper, the capacity of each core component of the micro-grid system under different combination paths such as Wind-PV power generation, battery energy storage, hydrogen production by electrolysis, and fuel cell power generation are optimized and economically analyzed. Taking the FCFF (Free Cash Flow of Firm) net present value maximization of the system running for 20 years as the objective function, considering the impact of energy shortage rate and dynamic electricity price, an operation research optimization model is established and intelligent algorithms are used to solve the model. The model can flexibly realize capacity optimization under different micro-grid combination paths, and it can prevent the solution result from falling into the local optimum through the design of quantum particle swarm algorithm. We analyzed the optimization results in terms of economic benefits, social benefits, and environmental benefits, and further analyzed the annual power generation status of the system and the operation status of the electrolysis hydrogen production system. The calculation example shows that under the current technical conditions, the micro-grid system composed of wind and solar power generation, electrochemical energy storage, and hydrogen production by electrolysis has better economic, social and environmental benefits than other models.