scholarly journals Prospect Prediction of Terminal Clean Power Consumption in China via LSSVM Algorithm Based on Improved Evolutionary Game Theory

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2065
Author(s):  
Shuxia Yang ◽  
Xianguo Zhu ◽  
Shengjiang Peng
Keyword(s):  
Game Theory ◽  
Energy Consumption ◽  
Power Consumption ◽  
Evolutionary Game Theory ◽  
Rapid Development ◽  
Evolutionary Game ◽  
Clean Energy ◽  
Machine Intelligence ◽  
Environmental Benefits ◽  
Support Vector

In recent years, China’s terminal clean power replacement construction has experienced rapid development, and China’s installed photovoltaic and wind energy capacity has soared to become the highest in the world. Precise and effective prediction of the scale of terminal clean power replacement can not only help make reasonable adjustments to the proportion of clean power capacity, but also promote the reduction of carbon emissions and enhance environmental benefits. In order to predict the prospects of China’s terminal clean energy consumption, first of all, the main factors affecting the clean power of the terminal are screened by using the grey revelance theory. Then, an evolutionary game theory (EGT) optimized least squares support vector machine (LSSVM) machine intelligence algorithm and an adaptive differential evolution (ADE) algorithm are applied in the example analysis, and empirical analysis shows that this model has a strong generalization ability, and that the prediction result is better than other models. Finally, we use the EGT–ADE–LSSVM combined model to predict China’s terminal clean energy consumption from 2019 to 2030, which showed that the prospect of China’s terminal clean power consumption is close to forty thousand billion KWh.

scholarly journals MAIAD: A Multistage Asymmetric Information Attack and Defense Model Based on Evolutionary Game Theory

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 215 ◽  
Author(s):  
Yu Yang ◽  
Bichen Che ◽  
Yang Zeng ◽  
Yang Cheng ◽  
Chenyang Li
Keyword(s):  
Game Theory ◽  
Asymmetric Information ◽  
Evolutionary Game Theory ◽  
Rapid Development ◽  
Evolutionary Game ◽  
Defense Strategy ◽  
Security Issues ◽  
Defense Model ◽  
Attack And Defense ◽  
Systems Simulation

With the rapid development and widespread applications of Internet of Things (IoT) systems, the corresponding security issues are getting more and more serious. This paper proposes a multistage asymmetric information attack and defense model (MAIAD) for IoT systems. Under the premise of asymmetric information, MAIAD extends the single-stage game model with dynamic and evolutionary game theory. By quantifying the benefits for both the attack and defense, MAIAD can determine the optimal defense strategy for IoT systems. Simulation results show that the model can select the optimal security defense strategy for various IoT systems.

Design and Simulation of Cloud Computing Intelligent Routing Algorithms Based on Optical Network

2019 ◽  
Vol 14 (12) ◽  
pp. 1717-1724
Author(s):  
Jing Tan
Keyword(s):  
Game Theory ◽  
Energy Consumption ◽  
Optical Networks ◽  
Wavelength Division Multiplexing ◽  
Evolutionary Game Theory ◽  
Optical Network ◽  
Routing Algorithm ◽  
Evolutionary Game ◽  
Routing Problem ◽  
Intelligent Routing

In the current communication technology, optical technology has been applied to the network to obtain optical network technology. Among them, optical network technology is optical wavelength division multiplexing (WDM), which can play a larger transmission capacity under lower energy consumption. Further breakthroughs in intelligent optical networks require improvements in routing issues. In this study, firstly, the optical network architecture is analyzed, including wavelength division multiplexing optical network and elastic optical network. Then, the routing problem in optical networks is analyzed, and the main factors affecting the routing problem are extracted. On the basis of studying the energy consumption characteristics of data centers and WDM optical networks, and considering the characteristics of cloud service configuration, evolutionary game theory and optical bypass theory are introduced to obtain an intelligent routing algorithm for cloud computing based on optical networks, and energy consumption tests are carried out on data transmission and processing. In order to reduce the overall energy consumption, the use of IP routers is reduced, and the idle data servers are shut down. Then, it is found that the total energy consumption increases slowly at different times. The energy consumption of evolutionary game theory is compared. Compared with non-evolutionary game theory, the optimized intelligent routing algorithm makes the energy consumption more stable, while reducing the use of servers can further reduce the good expenditure. The proposed algorithm is oriented to optical network, which solves the problem of low overall utilization of network resources and improves the service quality of cloud services.

A solution of dynamic VMs placement problem for energy consumption optimization based on evolutionary game theory

2015 ◽  
Vol 101 ◽  
pp. 260-272 ◽  
Author(s):  
Zhijiao Xiao ◽  
Jianmin Jiang ◽  
Yingying Zhu ◽  
Zhong Ming ◽  
Shenghua Zhong ◽  
...  
Keyword(s):  
Game Theory ◽  
Energy Consumption ◽  
Evolutionary Game Theory ◽  
Evolutionary Game ◽  
Placement Problem ◽  
Energy Consumption Optimization

scholarly journals A Study of Taxi Service Mode Choice Based on Evolutionary Game Theory

2019 ◽  
Vol 2019 ◽  
pp. 1-17
Author(s):  
Zhu Bai ◽  
Mingxia Huang ◽  
Shuai Bian ◽  
Huandong Wu
Keyword(s):  
Game Theory ◽  
Evolutionary Game Theory ◽  
Mode Choice ◽  
Evolutionary Game ◽  
Payoff Matrix ◽  
Game Model ◽  
Stable States ◽  
Taxi Service ◽  
Service Mode ◽  
The Impact

The emergence of online car-hailing service provides an innovative approach to vehicle booking but has negatively influenced the taxi industry in China. This paper modeled taxi service mode choice based on evolutionary game theory (EGT). The modes included the dispatching and online car-hailing modes. We constructed an EGT framework, including determining the strategies and the payoff matrix. We introduced different behaviors, including taxi company management, driver operation, and passenger choice. This allowed us to model the impact of these behaviors on the evolving process of service mode choice. The results show that adjustments in taxi company, driver, and passenger behaviors impact the evolutionary path and convergence speed of our evolutionary game model. However, it also reveals that, regardless of adjustments, the stable states in the game model remain unchanged. The conclusion provides a basis for studying taxi system operation and management.

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