GPS Signal Acquisition Based on an Improved Genetic Algorithm

2013 ◽  
Vol 291-294 ◽  
pp. 2909-2912
Author(s):  
Min Shi ◽  
Cong Cong Tian ◽  
Qing Ming Yi
Keyword(s):  
Genetic Algorithm ◽  
Initial Population ◽  
Signal Acquisition ◽  
Improved Genetic Algorithm ◽  
Search Range ◽  
Acquisition Performance ◽  
Signal Parameters ◽  
Dynamic Search ◽  
Gps Signal Acquisition ◽  
Crossover And Mutation

In order to solve the problem of GPS signal acquisition, an acquisition algorithm based on an improved genetic algorithm (IGA) is proposed in this paper. The IGA employs the technologies including the dynamic search range of parameter, the adaptive probabilities of crossover and mutation, and the small section method for generation of an initial population to search Doppler shift and code phase. Simulations and experiment results show that the proposed method can acquire the signal parameters precisely and rapidly. Consequently, the acquisition performance is improved.

The Application of an Improved Genetic Algorithm to Signal Acquisition

2014 ◽  
Vol 945-949 ◽  
pp. 2319-2322
Author(s):  
Min Shi ◽  
Cong Cong Tian ◽  
Qing Ming Yi ◽  
Song Li
Keyword(s):  
Genetic Algorithm ◽  
Initial Population ◽  
Signal Acquisition ◽  
Improved Genetic Algorithm ◽  
Search Range ◽  
Signal Parameters ◽  
Crossover Probability ◽  
Dynamic Search ◽  
Gps Signal Acquisition ◽  
Adaptive Crossover

The paper proposes an improved genetic algorithm which is applied to the problem of GPS signal acquisition.The improved genetic algorithm uses the small section method of the initial population, dynamic search range of parameter, and the adaptive crossover probability and mutation probability to search GPS signal parameters. Simulations and experiment results show that the improved genetic algorithm can search the signal parameters rapidly and precisely. The GPS signal acquisition performance is improved.

Optimization of PID Controller Parameters Based on Improved Genetic Algorithm

2013 ◽  
Vol 303-306 ◽  
pp. 1189-1192
Author(s):  
Ying Chen ◽  
Yong Jie Ma ◽  
Wen Xia Yun
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Optimization Design ◽  
Selection Strategy ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
New Approach ◽  
Effective Selection ◽  
System Requirements ◽  
Crossover And Mutation

The current Proportion Integration Differentiation(PID) optimization design methods are often difficult to consider the system requirements for quickness,reliability and robustness.So an Improved Genetic Algorithm(IGA) is proposed.The new method of generating the initial population,adaptive change of crossover and mutation probability and effective selection strategy are used to optimize the parameters of PID controller. The simulation experiments with Matlab prove the new approach is valid.

Simulation of the Pressure-Bearing Capacity Test Model in Architectural Decoration Design

2014 ◽  
Vol 716-717 ◽  
pp. 391-394
Author(s):  
Li Mei Guo ◽  
Ai Min Xiao
Keyword(s):  
Genetic Algorithm ◽  
Bearing Capacity ◽  
Test Method ◽  
Test Model ◽  
Improved Genetic Algorithm ◽  
Good Convergence ◽  
Mutation Operators ◽  
Process Pressure ◽  
Calculation Process ◽  
Crossover And Mutation

in architectural decoration process, pressure-bearing capacity test is the foundation of design, and is very important. To this end, a pressure-bearing capacity test method in architectural decoration design is proposed based on improved genetic algorithm. The selection, crossover and mutation operators in genetic algorithm are improved respectively. Using its fast convergence characteristics eliminate the pressure movement in the calculation process. The abnormal area of pressure-bearing existed in buildings which can ensure to be tested is added, to obtain accurate distribution information of the abnormal area of pressure-bearing. Simulation results show that the improved genetic algorithm has good convergence, can accurately test the pressure-bearing capacity in architectural decoration.

Study on the Improvement of Genetic Algorithm by Using Vehicle Routing Problem

2013 ◽  
Vol 365-366 ◽  
pp. 194-198 ◽  
Author(s):  
Mei Ni Guo
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Mathematic Model ◽  
Simulation Software ◽  
Initial Population ◽  
Planning Problem ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Search Speed ◽  
Vehicle Path

mprove the existing genetic algorithm, make the vehicle path planning problem solving can be higher quality and faster solution. The mathematic model for study of VRP with genetic algorithms was established. An improved genetic algorithm was proposed, which consist of a new method of initial population and partheno genetic algorithm revolution operation.Exploited Computer Aided Platform and Validated VRP by simulation software. Compared this improved genetic algorithm with the existing genetic algorithm and approximation algorithms through an example, convergence rate Much faster and the Optimal results from 117.0km Reduced to 107.8km,proved that this article improved genetic algorithm can be faster to reach an optimal solution. The results showed that the improved GA can keep the variety of cross and accelerate the search speed.

scholarly journals Genetic Algorithm-Based Variable Value Control Method for Solving the Ground Target Attacking Weapon-Target Allocation Problem

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Chao Wang ◽  
Guangyuan Fu ◽  
Daqiao Zhang ◽  
Hongqiao Wang ◽  
Jiufen Zhao
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Control Method ◽  
Search Space ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Mutation Strategy ◽  
Ground Target ◽  
Value Range ◽  
Target Allocation

Key ground targets and ground target attacking weapon types are complex and diverse; thus, the weapon-target allocation (WTA) problem has long been a great challenge but has not yet been adequately addressed. A timely and reasonable WTA scheme not only helps to seize a fleeting combat opportunity but also optimizes the use of weaponry resources to achieve maximum battlefield benefits at the lowest cost. In this study, we constructed a ground target attacking WTA (GTA-WTA) model and designed a genetic algorithm-based variable value control method to address the issue that some intelligent algorithms are too slow in resolving the problem of GTA-WTA due to the large scale of the problem or are unable to obtain a feasible solution. The proposed method narrows the search space and improves the search efficiency by constraining and controlling the variable value range of the individuals in the initial population and ensures the quality of the solution by improving the mutation strategy to expand the range of variables. The simulation results show that the improved genetic algorithm (GA) can effectively solve the large-scale GTA-WTA problem with good performance.

Research on Vehicle Routing Problem Models and Algorithms of Batteries Logistics Distribution for Electric Vehicle

2013 ◽  
Vol 791-793 ◽  
pp. 1409-1414 ◽  
Author(s):  
Meng Wang ◽  
Kai Liu ◽  
Zhu Long Jiang
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Electric Vehicle ◽  
Vehicle Routing Problem ◽  
Distribution Model ◽  
Initial Population ◽  
Mutation Operator ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Short Period

The battery quick exchange mode is an effective solution to resolve the battery charging problem of electric vehicle. For the electric vehicle battery distribution network with the battery quick exchange mode, the distribution model and algorithm are researched; the general mathematical model to take delivery of the vehicle routing problem with time window (VRP-SDPTW) is established. By analyzing the relationship between the main variables, structure priority function of the initial population, a new front crossover operator, swap mutation operator and reverse mutation operator are designed, and an improved genetic algorithm solving VRP-SDPTW is constructed. The algorithm could overcome the traditional genetic algorithm premature convergence defects. The example shows that the improved genetic algorithm can be effective in the short period of time to obtain the satisfactory solution of the VRP-SDPTW.

Transmission Network Expansion Planning under Improved Genetic Algorithm

2011 ◽  
Vol 347-353 ◽  
pp. 1458-1461
Author(s):  
Hong Fan ◽  
Yi Xiong Jin
Keyword(s):  
Genetic Algorithm ◽  
Linear Optimization ◽  
Large Integer ◽  
Transmission Network ◽  
Improved Genetic Algorithm ◽  
Network Expansion ◽  
Expansion Planning ◽  
Mutation Operators ◽  
Crossover And Mutation ◽  
Search Capability

Improved genetic algorithm for solving the transmission network expansion planning is presented in the paper. The module which considered the investment costs of new transmission facilities. It is a large integer linear optimization problem. In this work we present improved genetic algorithm to find the solution of excellent quality. This method adopts integer parameter encoded style and has nonlinear crossover and mutation operators, owns strong global search capability. Tests are carried out using a Brazilian Southern System and the results show the good performance.

Based on Improved Genetic Algorithm for Task Scheduling of Heterogeneous Multi-Core Processor

2014 ◽  
Vol 1030-1032 ◽  
pp. 1671-1675
Author(s):  
Yue Qiu ◽  
Jing Feng Zang
Keyword(s):  
Genetic Algorithm ◽  
Task Scheduling ◽  
Execution Time ◽  
Scheduling Algorithm ◽  
Experimental Results ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
High Quality ◽  
Multi Core Processor ◽  
Better Than

This paper puts forward an improved genetic scheduling algorithm in order to improve the execution efficiency of task scheduling of the heterogeneous multi-core processor system and give full play to its performance. The attribute values and the high value of tasks were introduced to structure the initial population, randomly selected a method with the 50% probability to sort for task of individuals of the population, thus to get high quality initial population and ensured the diversity of the population. The experimental results have shown that the performance of the improved algorithm was better than that of the traditional genetic algorithm and the HEFT algorithm. The execution time of tasks was reduced.

scholarly journals An Improved Genetic Algorithm with Initial Population Strategy for Symmetric TSP

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Yong Deng ◽  
Yang Liu ◽  
Deyun Zhou
Keyword(s):  
Genetic Algorithm ◽  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Traveling Salesman ◽  
Average Error ◽  
Initial Population ◽  
Improved Genetic Algorithm ◽  
Population Strategy ◽  
Series Of Experiments

A new initial population strategy has been developed to improve the genetic algorithm for solving the well-known combinatorial optimization problem, traveling salesman problem. Based on thek-means algorithm, we propose a strategy to restructure the traveling route by reconnecting each cluster. The clusters, which randomly disconnect a link to connect its neighbors, have been ranked in advance according to the distance among cluster centers, so that the initial population can be composed of the random traveling routes. This process isk-means initial population strategy. To test the performance of our strategy, a series of experiments on 14 different TSP examples selected from TSPLIB have been carried out. The results show that KIP can decrease best error value of random initial population strategy and greedy initial population strategy with the ratio of approximately between 29.15% and 37.87%, average error value between 25.16% and 34.39% in the same running time.

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.

Sign in / Sign up

Export Citation Format

Share Document