scholarly journals Prototype Filter Design Based on Channel Estimation for FBMC/OQAM Systems

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Yongjin Liu ◽  
Xihong Chen ◽  
Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Channel Estimation ◽  
Optimization Problem ◽  
Filter Design ◽  
Stop Band ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Prototype Filter ◽  
Global Optimal

A prototype filter design for FBMC/OQAM systems is proposed in this study. The influence of both the channel estimation and the stop-band energy is taken into account in this method. An efficient preamble structure is proposed to improve the performance of channel estimation and save the frequency spectral efficiency. The reciprocal of the signal-to-interference plus noise ratio (RSINR) is derived to measure the influence of the prototype filter on channel estimation. After that, the process of prototype filter design is formulated as an optimization problem with constraint on the RSINR. To accelerate the convergence and obtain global optimal solution, an improved genetic algorithm is proposed. Especially, the History Network and pruning operator are adopted in this improved genetic algorithm. Simulation results demonstrate the validity and efficiency of the prototype filter designed in this study.

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.

Improved Genetic Algorithm for Gas Well Production Dynamic Optimization Model

2012 ◽  
Vol 524-527 ◽  
pp. 1178-1184
Author(s):  
Xian Feng Ding
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Optimization ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Gas Well ◽  
Well Production ◽  
Dynamic Optimization Model ◽  
Global Optimal ◽  
Production Dynamic

In this paper, An improved genetic algorithm that Powell genetic annealing exact penalty function method was proposed to deal with the non-linear characteristics in gas well production dynamic optimization model. The direct method with Powell to unconstrained optimization problems as a parallel operator as selection, crossover and mutation operator, was embedded into the basic genetic algorithm. Powell operator was defined in genetic algorithm and using annealing exact penalty function control penalty term, so the hybrid genetic algorithm for the global optimal solution to unconstrained optimization problem was made. The method avoided the difficulties of solving the model gradient, and effectively overcome searching local optimal solution and not high success probability by artificially giving more initial point to calculate and seek the optimal solution with Powell method. And the method significantly improved the convergence probability of the global optimal solution in the genetic algorithm. The method was practical and effective by analysis of examples, and it can give suggestion for gas well reasonable production system.

The Optimization Design of Multistage Hybrid Planetary Transmission Based on Improved Genetic Algorithm

2014 ◽  
Vol 532 ◽  
pp. 422-426
Author(s):  
Ji Ming Tian ◽  
Xin Tan
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Global Optimal Solution ◽  
Global Optimal ◽  
Driving Stability ◽  
Planetary Transmission ◽  
Minimum Volumes ◽  
Pseudo Parallel Genetic Algorithm

According to the characteristics of genetic algorithm, an improved method combined dynamic penalty function with pseudo-parallel genetic algorithm is presented in this paper and it can overcome the disadvantages of genetic algorithm for improving the efficiency of algorithm. The improved genetic algorithm is applied to optimization design of multistage hybrid planetary transmission. It takes the minimum volumes as object functions, and fully considered such constraint condition. 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. Therefore, the size parameters are optimized, as much as the driving stability and efficiency. Compared to the original program, the volume of 16.55% is decreased.

Improved Method of Hybrid Genetic Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 4014-4017
Author(s):  
Lei Ding ◽  
Yong Jun Luo ◽  
Yang Yang Wang ◽  
Zheng Li ◽  
Bing Yin Yao
Keyword(s):  
Genetic Algorithm ◽  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Local Optimum ◽  
Global Optimal Solution ◽  
Global Optimal ◽  
Better Than

On account of low convergence of the traditional genetic algorithm in the late,a hybrid genetic algorithm based on conjugate gradient method and genetic algorithm is proposed.This hybrid algorithm takes advantage of Conjugate Gradient’s certainty, but also the use of genetic algorithms in order to avoid falling into local optimum, so it can quickly converge to the exact global optimal solution. Using Two test functions for testing, shows that performance of this hybrid genetic algorithm is better than single conjugate gradient method and genetic algorithm and have achieved good results.

Genetic Algorithm Based on Permutation Distance for Laser Antimissile Problem

2010 ◽  
Vol 40-41 ◽  
pp. 488-493
Author(s):  
Yong Sun ◽  
Mao Rui Zhang ◽  
Wei Wei Liu ◽  
Li Na Zhang ◽  
He Li
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Global Optimal Solution ◽  
The Real ◽  
Real Application ◽  
Global Optimal ◽  
Simulation Results

The genetic algorithm based on permutation distance definition is used to solve the laser antimissile system. When faced with multiple attacking targets, it is clearly important for the laser antimissile system to determine the sequence of the attacking targets to be intercepted so that the maximum attacking targets are destroyed. It’s very difficult to find the global optimal solution, especially when the number of the targets is greater than six. The permutation distance definition is introduced to measure the distribution of the population. The successive zeros permutation distance is to stop the genetic algorithm iterations. Finally, taking ten targets as an example, the simulation results show that the convergence of the algorithm is fast and this achievement can be used in the real application.

Yarn Selection Based on Simulated Annealing Genetic Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 1548-1551
Author(s):  
Li Jiang ◽  
Gang Feng Yan ◽  
Zhen Fan
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Original Image ◽  
Separation Algorithm ◽  
Global Optimal Solution ◽  
Simulated Annealing Genetic Algorithm ◽  
Color Separation ◽  
Global Optimal

Aiming at the bad performance when achieve rich colors of fabric with very limited yarns in the traditional woven industry, the paper comes up with a solution of selecting yarn from a set of yarns based on SAGA(simulated annealing genetic algorithm). In order to reduce the computational complexity, original image is compressed based on clustering algorithm. And the original yarns is divided into four regions based on color separation algorithm to narrow the feasible area. The result of experiments show that image compression and yarns division can greatly improve the speed of SAGA, and SAGA can effectively converges to global optimal solution.

scholarly journals Secrecy Wireless-Powered Sensor Networks for Internet of Things

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Junxia Li ◽  
Hui Zhao ◽  
Xueyan Chen ◽  
Zheng Chu ◽  
Li Zhen ◽  
...  
Keyword(s):  
Time Allocation ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Access Point ◽  
Case Scenario ◽  
Secrecy Rate ◽  
Global Optimal Solution ◽  
Worst Case ◽  
Sdp Relaxation ◽  
Global Optimal

This paper investigates a secure wireless-powered sensor network (WPSN) with the aid of a cooperative jammer (CJ). A power station (PS) wirelessly charges for a user equipment (UE) and the CJ to securely transmit information to an access point (AP) in the presence of multiple eavesdroppers. Also, the CJ are deployed, which can introduce more interference to degrade the performance of the malicious eavesdroppers. In order to improve the secure performance, we formulate an optimization problem for maximizing the secrecy rate at the AP to jointly design the secure beamformer and the energy time allocation. Since the formulated problem is not convex, we first propose a global optimal solution which employs the semidefinite programming (SDP) relaxation. Also, the tightness of the SDP relaxed solution is evaluated. In addition, we investigate a worst-case scenario, where the energy time allocation is achieved in a closed form. Finally, numerical results are presented to confirm effectiveness of the proposed scheme in comparison to the benchmark scheme.

Task Scheduling of Parallel Development Projects Using Genetic Algorithms

2004 ◽  
Author(s):  
Miao Zhuang ◽  
Ali A. Yassine
Keyword(s):  
Genetic Algorithm ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Local Optimum ◽  
Development Projects ◽  
Scheduling Problems ◽  
Global Optimal Solution ◽  
Genetic Algorithm Approach ◽  
Project Scheduling Problem ◽  
Global Optimal

Resources for development projects are often scarce in the real world. Generally, many projects are to be completed that rely on a common pool of resources. Besides resource constraints, there exists data dependency among tasks within each project. A genetic algorithm approach with one-point uniform crossover and a refresh operator is proposed to minimize the overall duration or makespan of multiple projects in a resource constrained multi project scheduling problem (RCMPSP) without violating inter-project resource constraints or intra-project precedence constraints. The proposed GA incorporates stochastic feedback or rework of tasks. It has the capability of capturing the local optimum for each generation and therefore ensuring a global best solution. The proposed Genetic Algorithm, with several variants of GA parameters is tested on sample scheduling problems with and without stochastic feedback. This algorithm demonstrates to provide a quick convergence to a global optimal solution and detect the most likely makespan range for parallel projects of tasks with stochastic feedback.

Chaotic Mutation Bee Evolution Algorithm

2012 ◽  
Vol 482-484 ◽  
pp. 1636-1639
Author(s):  
Yuan Yao ◽  
Yan Ling Zou ◽  
Qi Man Wu ◽  
Zhong Ren Guan
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Fast Convergence ◽  
Evolution Process ◽  
Global Optimal Solution ◽  
Iterative Calculation ◽  
Global Optimal ◽  
Evolution Algorithm ◽  
The Individual

In order to make full use of chaotic mutation genetic algorithm and the chaotic mutation and bee evolution algorithm, the characteristics of the two algorithms, and the combination of chaotic mutation bee evolution algorithm is proposed. The algorithm in bee evolution process, to adapt to the value of group of smaller portions of the variation of individuals to chaos; to adapt to the value of group of large part of the individual, to the best individual as the center, change crossover operation, each generation is the best individual immune evolutionary iterative calculation. Thus, as the iteration, the algorithm not only fast convergence, and can also by a higher accuracy by the global optimal solution.

scholarly journals A general framework of particle swarm optimization

2021 ◽  
Author(s):  
Loc Nguyen
Keyword(s):  
Particle Swarm Optimization ◽  
General Framework ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Target Function ◽  
Optimal Solution ◽  
Exploration And Exploitation ◽  
Global Optimal Solution ◽  
Swarm Optimization ◽  
Global Optimal

Particle swarm optimization (PSO) is an effective algorithm to solve the optimization problem in case that derivative of target function is inexistent or difficult to be determined. Because PSO has many parameters and variants, I propose a general framework of PSO called GPSO which aggregates important parameters and generalizes important variants so that researchers can customize PSO easily. Moreover, two main properties of PSO are exploration and exploitation. The exploration property aims to avoid premature converging so as to reach global optimal solution whereas the exploitation property aims to motivate PSO to converge as fast as possible. These two aspects are equally important. Therefore, GPSO also aims to balance the exploration and the exploitation. It is expected that GPSO supports users to tune parameters for not only solving premature problem but also fast convergence.

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