Traffic light signal parameters optimization using particle swarm optimization

2015 ◽  
Author(s):  
I Gede Pasek Suta Wijaya ◽  
Keiichi Uchimura ◽  
Gou Koutaki
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Light Signal ◽  
Parameters Optimization ◽  
Traffic Light ◽  
Swarm Optimization ◽  
Signal Parameters

scholarly journals Traffic Light Signal Parameters Optimization using Modification of Multielement Genetic Algorithm

2018 ◽  
Vol 8 (1) ◽  
pp. 246 ◽  
Author(s):  
I Gede Pasek Suta Wijaya ◽  
Keeichi Uchimura ◽  
Gou Koutaki
Keyword(s):  
Genetic Algorithm ◽  
Traffic Congestion ◽  
Road Network ◽  
Hash Table ◽  
Optimization Methods ◽  
Light Signal ◽  
Parameters Optimization ◽  
Traffic Light ◽  
Swarm Optimization ◽  
Signal Parameters

A strategy to optimize traffic light signal parameters is presented for solving traffic congestion problem using modification of the Multielement Genetic Algorithm (MEGA). The aim of this method is to improve the lack of vehicle throughput (FF ) of the works called as traffic light signal parameters optimization using the MEGA and Particle Swarm Optimization (PSO). In this case, the modification of MEGA is done by adding Hash-Table for saving some best populations for accelerating the recombination process of MEGA which is shortly called as H-MEGA. The experimental results show that the H-MEGA based optimization provides better performance than MEGA and PSO based methods (improving the FF of both MEGA and PSO based optimization methods by about 10.01% (from 82,63% to 92.64%) and 6.88% (from 85.76% to 92.64%), respectively). In addition, the H-MEGA improve significantly the real FF of Ooe Toroku road network of Kumamoto City, Japan about 21.62%.

Method of Optimization for Target Localization Model Parameters Based on LSSVR

2011 ◽  
Vol 268-270 ◽  
pp. 934-939
Author(s):  
Xue Wen He ◽  
Gui Xiong Liu ◽  
Hai Bing Zhu ◽  
Xiao Ping Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Target Localization ◽  
Likelihood Method ◽  
Support Vector ◽  
Swarm Optimization ◽  
Localization Accuracy ◽  
Localization Method

Aiming at improving localization accuracy in Wireless Sensor Networks (WSN) based on Least Square Support Vector Regression (LSSVR), making LSSVR localization method more practicable, the mechanism of effects of the kernel function for target localization based on LSSVR is discussed based on the mathematical solution process of LSSVR localization method. A novel method of modeling parameters optimization for LSSVR model using particle swarm optimization is proposed. Construction method of fitness function for modeling parameters optimization is researched. In addition, the characteristics of particle swarm parameters optimization are analyzed. The computational complexity of parameters optimization is taken into consideration comprehensively. Experiments of target localization based on CC2430 show that localization accuracy using LSSVR method with modeling parameters optimization increased by 23%~36% in compare with the maximum likelihood method(MLE) and the localization error is close to the minimum with different LSSVR modeling parameters. Experimental results show that adapting a reasonable fitness function for modeling parameters optimization using particle swarm optimization could enhance the anti-noise ability significantly and improve the LSSVR localization performance.

Parameter Identification Method for Turbine Speed Governor System Based on Particle Swarm Optimization

2013 ◽  
Vol 448-453 ◽  
pp. 2511-2515
Author(s):  
Wen Sun ◽  
Xiang Yu Kong ◽  
Qun Yang ◽  
Fang Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Evolutionary Strategy ◽  
Identification System ◽  
Swarm Optimization ◽  
Identification Method ◽  
Speed Governor ◽  
Speed Governor System

A parameter identification method for generator speed governor system, which combines decoupling parameter identification and overall recognition with measured data, was proposed in the paper. The method bases on particle swarm optimization, and takes parameter identification as a parameters optimization problem under evaluation function. According to an intelligent optimization algorithms evolutionary strategy, the individual's status is continuously adjusted until the identification system and actual system output error is sufficiently small. Case studies show that the proposed method can be applied to the measured parameters and model validation work.

scholarly journals Turning Parameters Optimization Using Particle Swarm Optimization

2014 ◽  
Vol 69 ◽  
pp. 670-677 ◽  
Author(s):  
Hrelja Marko ◽  
Klancnik Simon ◽  
Irgolic Tomaz ◽  
Paulic Matej ◽  
Balic Joze ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Parameters Optimization ◽  
Swarm Optimization
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