A New Testability Optimization Allocation Approach

2013 ◽  
Vol 328 ◽  
pp. 444-449 ◽  
Author(s):  
Gang Liu ◽  
Fang Li
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Iterative Process ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Numerical Example ◽  
Set Up ◽  
Optimization Allocation ◽  
Allocation Approach

This paper describes a methodology based on improved genetic algorithms (GA) and experiments plan to optimize the testability allocation. Test resources were reasonably configured for testability optimization allocation, in order to meet the testability allocation requirements and resource constraints. The optimal solution was not easy to solve of general genetic algorithm, and the initial parameter value was not easy to set up and other defects. So in order to more efficiently test and optimize the allocation, migration technology was introduced in the traditional genetic algorithm to optimize the iterative process, and initial parameters of algorithm could be adjusted by using AHP approach, consequently testability optimization allocation approach based on improved genetic algorithm was proposed. A numerical example is used to assess the method. and the examples show that this approach can quickly and efficiently to seek the optimal solution of testability optimization allocation problem.

The Simulation and Analyses of Timing-Optimal Trajectory Planning for 6DOF Robot

2014 ◽  
Vol 716-717 ◽  
pp. 1555-1558
Author(s):  
Zhi Jian Gou
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Optimal Solution ◽  
Global Search ◽  
Search Performance ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Optimal Trajectory Planning

The algorithm has been improved to the adaptive genetic operators and flow based on the basic theory of simple genetic algorithm and adopted elitism strategy to select the best individual for iterative operation. The improved genetic algorithm not only ensured better global search performance, but also improved the convergent speed. The optimal solution was obtained and simulated by the improved genetic algorithms under the kinematical constraints.

The Research of Timing-Optimal Trajectory Planning Based on Improved Genetic Algorithms

2013 ◽  
Vol 433-435 ◽  
pp. 562-565 ◽  
Author(s):  
Zhi Jian Gou
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Trajectory Planning ◽  
Optimal Trajectory ◽  
Optimal Solution ◽  
Search Performance ◽  
Improved Genetic Algorithm ◽  
Genetic Operators ◽  
Operation Process ◽  
Optimal Trajectory Planning

The algorithm has been improved to the adaptive genetic operators and flow based on the basic theory of simple genetic algorithm and adopted elitism strategy to select the best individual for iterative operation. Program the operation process in the MATLAB software. The improved genetic algorithm not only ensured better global search performance, but also improved the convergent speed. The optimal solution was obtained by the improved genetic algorithms under the kinematical constraints.

Multi-Objective Optimization of the Housing’s Fillet Angle of the Universal Rolling Mills Using Improved Genetic Algorithm Combined with FEM

2010 ◽  
Vol 143-144 ◽  
pp. 1370-1374
Author(s):  
Jin Hong Ma ◽  
Wen Zhi Zhang ◽  
Wei Chen ◽  
Shi Ping Chao
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optimal Solution ◽  
Optimization Method ◽  
Order Method ◽  
Improved Genetic Algorithm ◽  
Optimal Method ◽  
Rolling Mills ◽  
Multi Objective ◽  
First Order Method

In this paper, the standard genetic algorithm is modified based on the deduction of Rolle theory. A kind of genetic algorithms is set up. Combined the modified genetic algorithms with finite element method, a new kind engineering optimization method is established. This optimal method takes fully advantage of the unique feature of genetic algorithms that the optimal solution is achieved by a rapid global search and takes advantage of the unique features of FEM that the computation is accuracy. This method can solve multi-objective problems with more complex constraints in the engineering. The procedure is developed by VC++. Housing’s fillet angle of the universal rolling mills is optimized using this method. Compared with the zero order method and first order method companied with ANSYS, the result of this optimizing method is better.

scholarly journals FEM-Validated Optimal Design of Laminate Process Parameters Based on Improved Genetic Algorithm

2022 ◽  
Vol 6 (1) ◽  
pp. 21
Author(s):  
Xing Mou ◽  
Zhiqiang Shen ◽  
Honghao Liu ◽  
Hui Xv ◽  
Xianzhao Xia ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Carbon Fibre ◽  
Process Parameters ◽  
Iterative Process ◽  
Mechanical Analysis ◽  
Strength Ratio ◽  
Improved Genetic Algorithm ◽  
Search Range ◽  
Before And After

In tape placement process, the laying angle and laying sequence of laminates have proven their significant effects on the mechanical properties of carbon fibre reinforced composite material, specifically, laminates. In order to optimise these process parameters, an optimisation algorithm is developed based on the principles of genetic algorithms for improving the precision of traditional genetic algorithms and resolving the premature phenomenon in the optimisation process. Taking multi-layer symmetrically laid carbon fibre laminates as the research object, this algorithm adopts binary coding to conduct the optimisation of process parameters and mechanical analysis with the laying angle as the design variable and the strength ratio R as the response variable. A case study was conducted and its results were validated by the finite element analyses. The results show that the stresses before and after optimisation are 116.0 MPa and 100.9 MPa, respectively, with a decrease of strength ratio by 13.02%. The results comparison indicates that, in the iterative process, the search range is reduced by determining the code and location of important genes, thereby reducing the computational workload by 21.03% in terms of time consumed. Through multiple calculations, it validates that “gene mutation” is an indispensable part of the genetic algorithm in the iterative process.

Solving vehicle routing problem by using improved genetic algorithm for optimal solution

2017 ◽  
Vol 21 ◽  
pp. 255-262 ◽  
Author(s):  
Mazin Abed Mohammed ◽  
Mohd Khanapi Abd Ghani ◽  
Raed Ibraheem Hamed ◽  
Salama A. Mostafa ◽  
Mohd Sharifuddin Ahmad ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Routing Problem

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.

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.

Research on the Optimization of the Numerical Value Based on Improved Genetic Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1256-1260
Author(s):  
Zhen Dong Li ◽  
Qi Yi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Adaptive Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
Adaptive Genetic Algorithms ◽  
Crossover Probability ◽  
Adaptive Crossover ◽  
Searching Ability ◽  
New Algorithms ◽  
Relevance Index

For the lack of crossover operation, from three aspects of crossover operation , systemically proposed one kind of improved Crossover operation of Genetic Algorithms, namely used a kind of new consistent Crossover Operator and determined which two individuals to be paired for crossover based on relevance index, which can enhance the algorithms global searching ability; Based on the concentrating degree of fitness, a kind of adaptive crossover probability can guarantee the population will not fall into a local optimal result. Simulation results show that: Compared with the traditional cross-adaptive genetic Algorithms and other adaptive genetic algorithm, the new algorithms convergence velocity and global searching ability are improved greatly, the average optimal results and the rate of converging to the optimal results are better.

An Improved Cloud Adaptive Genetic Algorithm Based on Cloud Computing for Active Optimization Calculation

2015 ◽  
Vol 713-715 ◽  
pp. 1579-1582
Author(s):  
Shao Min Zhang ◽  
Ze Wu ◽  
Bao Yi Wang
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Large Scale ◽  
Optimal Solution ◽  
Adaptive Genetic Algorithm ◽  
Load Optimization ◽  
Active Load ◽  
Optimization Calculation ◽  
Local Optimal Solution ◽  
Optimization Allocation

Under the background of huge amounts of data in large-scale power grid, the active power optimization calculation is easy to fall into local optimal solution, and meanwhile the calculation demands a higher processing speed. Aiming at these questions, the farmer fishing algorithm which is applied to solve the problem of optimal distribution of active load for coal-fired power units is used to improve the cloud adaptive genetic algorithm (CAGA) for speeding up the convergence phase of CAGA. The concept of cloud computing algorithm is introduced, and parallel design has been done through MapReduce graphs. This method speeds up the calculation and improves the effectiveness of the active load optimization allocation calculation.

scholarly journals An Improved Genetic Algorithm for Emergency Decision Making under Resource Constraints Based on Prospect Theory

Algorithms ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 43 ◽  
Author(s):  
Leiwen Chen ◽  
Yingming Wang ◽  
Geng Guo
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Prospect Theory ◽  
Resource Constraints ◽  
Emergency Situation ◽  
Practical Significance ◽  
Improved Genetic Algorithm ◽  
Emergency Decision Making ◽  
Emergency Decision ◽  
Value Measurement

The study of emergency decision making (EDM) is helpful to reduce the difficulty of decision making and improve the efficiency of decision makers (DMs). The purpose of this paper is to propose an innovative genetic algorithm for emergency decision making under resource constraints. Firstly, this paper analyzes the emergency situation under resource constraints, and then, according to the prospect theory (PT), we further propose an improved value measurement function and an emergency loss levels weighting algorithm. Secondly, we assign weights for all emergency locations using the best–worst method (BWM). Then, an improved genetic algorithm (GA) based on prospect theory (PT) is established to solve the problem of emergency resource allocation between multiple emergency locations under resource constraints. Finally, the analyses of example show that the algorithm can shorten the decision-making time and provide a better decision scheme, which has certain practical significance.

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