Research On Optimization Method For Project Site Selection Based On Improved Genetic Algorithm

2020 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Ling min Yang ◽  
Zhong min Tang ◽  
Sijun Liu
Keyword(s):  
Genetic Algorithm ◽  
Site Selection ◽  
Optimization Method ◽  
Improved Genetic Algorithm ◽  
Project Site

Research on Coordination of Evacuation of Human and Car Simulation Optimization Method

2011 ◽  
Vol 130-134 ◽  
pp. 2185-2189
Author(s):  
Yun Long Ma ◽  
Jian Wang
Keyword(s):  
Genetic Algorithm ◽  
Simulation Optimization ◽  
Optimization Method ◽  
Crowd Simulation ◽  
Optimal Scheduling ◽  
Regular Pattern ◽  
Improved Genetic Algorithm ◽  
Whole Process ◽  
Evacuation Strategy ◽  
Coordination System

Among researches on people evacuation under emergency, there are no deep touch of coordinating evacuation strategy of human and traffic, and also can not set models and simulate the whole process of evacuation. This research focused on the human evacuation under emergencies, divided the situations into Not Intervene and May Intervene, analyzed the coordination system and probed the model of coordinating evacuation. Based on the result of crowd simulation, the author could forecast the result of human evacuation and find the optimal traffic schedule proposal. The author took use of the improved Genetic Algorithm as well as the distributed coordinating simulation, finally found a systematic optimal scheduling program and revealed the internal regular pattern of coordinating evacuation.

An Improved Hybrid Genetic Algorithm and Performance Study

2012 ◽  
Vol 482-484 ◽  
pp. 95-98
Author(s):  
Wei Dong Ji ◽  
Ke Qi Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Convergence Speed ◽  
Test Results ◽  
Improved Genetic Algorithm ◽  
Performance Study ◽  
Speed Up ◽  
And Performance

Put forward a kind of the hybrid improved genetic algorithm of particle swarm optimization method (PSO) combine with and BFGS algorithm of, this method using PSO good global optimization ability and the overall convergence of BFGS algorithm to overcome the blemish of in the conventional algorithm slow convergence speed and precocious and local convergence and so on. Through the three typical high dimensional function test results show that this method not only improved the algorithm of the global search ability, to speed up the convergence speed, but also improve the quality of the solution and its reliability of optimization results.

The Optimization Design of Wind Turbine Gearbox Based on Improved Genetic Algorithm and Feasibility Analysis

2014 ◽  
Vol 889-890 ◽  
pp. 107-112
Author(s):  
Ji Ming Tian ◽  
Xin Tan
Keyword(s):  
Genetic Algorithm ◽  
Wind Turbine ◽  
Optimization Design ◽  
Optimal Solution ◽  
Optimization Method ◽  
Strength Analysis ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Wind Turbine Gearbox ◽  
Global Optimal

The design of the gearbox must ensure the simplest structure and the lightest weight under the premise of meeting the reliability and life expectancy. According to the requirement of wind turbine, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is used to optimize gearbox. It takes the minimum volumes as object functions. It is showed that the ability to search the global optimal solution of improved genetic algorithm and less number of iterations. The global optimal solution is worked out quickly. The size parameters are optimized, as much as the driving stability and efficiency. To verify the feasibility of improved genetic algorithm, ring gear of the gearbox is analyzed. Static strength analysis shows that the optimization method is reasonable and effective.

Optimal Sensor Placement Based on MAC and SPGA Algorithms

2012 ◽  
Vol 594-597 ◽  
pp. 1118-1122 ◽  
Author(s):  
Yong Ming Fu ◽  
Ling Yu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Manipulation ◽  
Optimization Method ◽  
Frame Structure ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Modal Assurance Criterion ◽  
Sensor Optimization ◽  
Computation Efficiency ◽  
The Individual

In order to solve the problem on sensor optimization placement in the structural health monitoring (SHM) field, a new sensor optimization method is proposed based on the modal assurance criterion (MAC) and the single parenthood genetic algorithm (SPGA). First, the required sensor numbers are obtained by using the step accumulating method. The SPGA is used to place sensors, in which the binary coding is adopted to realize the genetic manipulation through gene exchange, gene shift and gene inversion. Then, the method is further simplified and improved for higher computation efficiency. Where, neither the individual diversity of initial population nor the immature convergence problem is required. Finally, a numerical example of 61 truss frame structure is used to assess the robustness of the proposed method. The illustrated results show that the new method is better than the improved genetic algorithm and the step accumulating method in the search capacity, computational efficiency and reliability.

scholarly journals Taxiing Route Scheduling between Taxiway and Runway in Hub Airport

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Yu Jiang ◽  
Xinxing Xu ◽  
Honghai Zhang ◽  
Yuxiao Luo
Keyword(s):  
Genetic Algorithm ◽  
Delay Time ◽  
Optimization Method ◽  
Improved Genetic Algorithm ◽  
Route Length ◽  
Optimizing Model ◽  
The Right ◽  
Hub Airport ◽  
Runway Scheduling ◽  
Route Scheduling

To guarantee the operation safety of airport, improve the efficiency of surface operation, and enhance the fairness of taxiing route scheduling, an optimizing model is established for the airport surface taxiing route scheduling. Reducing the total aircraft taxiing route length and reducing the waiting delay time are the goals of the model by controlling the initial taxiing time of aircraft and choosing the right taxiing route. The model can guarantee the continuous taxiing for all aircraft without conflicts. The runway scheduling is taken into consideration in the model to optimize the surface operation. The improved genetic algorithm is designed for simulation and validation. The simulation results show that compared with the ant colony optimization method, the improved genetic algorithm reduces the total extra taxiing distance by 47.8% and the total waiting delay time decreases by 21.5%. The optimization model and improved genetic algorithm are feasible. The optimization of taxiing route method can provide decision support for hub airports.

scholarly journals Intersection signal timing optimization considering the travel safety of the elderly

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401989721 ◽  
Author(s):  
Changxi Ma ◽  
Pengfei Liu
Keyword(s):  
Genetic Algorithm ◽  
Control Method ◽  
The Elderly ◽  
Calibration Method ◽  
Optimization Method ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Improved Genetic Algorithm ◽  
Signal Ratio ◽  
Vehicle Delay

With the rapid growth of the elderly population in China, the proportion of middle-aged and elderly pedestrians crossing streets at signalized intersections has been increasing gradually, mandating the consideration of the crossing characteristics and travel safety of the elderly in signal matching. This article proposes a new signal control parameter optimization method for intersections based on an improved genetic algorithm. According to the crossing characteristics and travel safety of the elderly, the average vehicle delay is used as the control objective, and the green signal ratio and cycle time are used as control variables. The improved genetic algorithm with an improved fitness calibration method and an adaptive cross-mutation function is used to solve the signal control model. Based on the optimization analysis of traffic signal control parameters at a traffic intersection, the study shows that the improved signal control method can effectively reduce the average vehicle delay compared to the Webster algorithm and the traditional genetic algorithm.

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