Spatial public goods game with continuous contributions based on Particle Swarm Optimization learning and the evolution of cooperation

2018 ◽  
Vol 505 ◽  
pp. 973-983 ◽  
Author(s):  
Ji Quan ◽  
Xiukang Yang ◽  
Xianjia Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Public Goods ◽  
Particle Swarm ◽  
Evolution Of Cooperation ◽  
Public Goods Game ◽  
Swarm Optimization

USING PARTICLE SWARM OPTIMIZATION TO EVOLVE COOPERATION IN MULTIPLE CHOICES ITERATED PRISONER'S DILEMMA GAME

2013 ◽  
Vol 12 (03) ◽  
pp. 1350013 ◽  
Author(s):  
XIAOYANG WANG ◽  
YANG YI ◽  
HUIYOU CHANG ◽  
YIBIN LIN
Keyword(s):  
Particle Swarm Optimization ◽  
Prisoner's Dilemma ◽  
Particle Swarm ◽  
Prisoner’S Dilemma ◽  
Evolution Of Cooperation ◽  
Swarm Optimization ◽  
Iterated Prisoner's Dilemma ◽  
Formidable Challenge ◽  
The Impact ◽  
Iterated Prisoner’S Dilemma

Mechanisms of promoting the evolution of cooperation in two-player, two-strategy evolutionary games have been discussed in great detail over the past decades. Understanding the effects of repeated interactions in n-player with n-choice is a formidable challenge. This paper presents and investigates the application of co-evolutionary training techniques based on particle swarm optimization (PSO) to evolve cooperation for the iterated prisoner's dilemma (IPD) game with multiple choices. Several issues will be addressed, which include the evolution of cooperation and the evolutionary stability in the presence of multiple choices and noise. First is using PSO approach to evolve cooperation. The second is the consideration of real-dilemma between social cohesion and individual profit. Experimental results show that the PSO approach evolves the cooperation. Agents with stronger social cognition choose higher levels of cooperation. Finally the impact of noise on the evolution of cooperation is examined. Experiments show the noise has a negative impact on the evolution of cooperation.

Smart teaching mode based on particle swarm image recognition and human-computer interaction deep learning

2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao
Keyword(s):  
Feature Extraction ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Real Time ◽  
Image Recognition ◽  
Particle Swarm ◽  
Learning Technology ◽  
Search Performance ◽  
Swarm Optimization ◽  
Teaching Mode

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Adaptive Inertia Weight Particle Swarm Optimization For Linear and Nonlinear Channel Equalization

2012 ◽  
Vol 3 (4) ◽  
pp. 1-4
Author(s):  
Diana D.C Diana D.C ◽  
◽  
Joy Vasantha Rani.S.P Joy Vasantha Rani.S.P ◽  
Nithya.T.R Nithya.T.R ◽  
Srimukhee.B Srimukhee.B
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Channel Equalization ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Linear And Nonlinear
Sign in / Sign up

Export Citation Format

Share Document