Research on Genetic Algorithm in Mold Optimization Design

2013 ◽  
Vol 397-400 ◽  
pp. 1030-1033
Author(s):  
Xi Chen ◽  
Bao Sheng Zhao
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Evolution Model ◽  
Crossover Operator ◽  
Improved Genetic Algorithm ◽  
Design Variables ◽  
Species Evolution ◽  
Population Genetic Algorithm

Species evolution model in natural is introduced into the genetic algorithm to reflect the true laws of evolution. A multi-population genetic algorithm based on species evolution is developed. In the algorithm, the parameters of species evolution model are considered as design variables, and the equation is regarded as modified arithmetic crossover operator to participate in genetic operation. Immigration operator is used to promote convergence and enhance the ability of search optimal solution. The improved genetic algorithm is applied to mold optimization design to search the optimal gate location. The examples indicate that this algorithm can effectively solve the mold optimization problem.

The Application of Improved Genetic Algorithm in Gate Location Optimization of Plastic Injection Molding

2013 ◽  
Vol 694-697 ◽  
pp. 2721-2724
Author(s):  
Xi Chen ◽  
Xi Cheng Wang
Keyword(s):  
Genetic Algorithm ◽  
Injection Molding ◽  
Optimal Solution ◽  
Plastic Injection Molding ◽  
Improved Genetic Algorithm ◽  
Gate Location ◽  
Design Variables ◽  
Population Genetic Algorithm ◽  
Plastic Injection ◽  
Species Equation

A multi-population genetic algorithm based on species equation and Kriging operator is presented in this paper. The parameters of species equation are considered as design variables and processed by real coding, the equation is regarded as modified arithmetic crossover operator to participate in genetic operation. The Kriging operator is bought in to enhance the ability of search optimal solution and promote convergence. The improved genetic algorithm, combined with Z-MOLD simulation program, is used to search the optimal gate location. The results show that the algorithm can effectively solve the plastic injection molding 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.

Genetic algorithm-based optimization design method of the Formula SAE racing car’s rear wing

2017 ◽  
Vol 232 (7) ◽  
pp. 1255-1269 ◽  
Author(s):  
Hui Wang ◽  
Qiuyang Bai ◽  
Xufei Hao ◽  
Lin Hua ◽  
Zhenghua Meng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Formula Sae ◽  
Design Variables ◽  
Rear Wing ◽  
Sample Points ◽  
Lift And Drag

The aerodynamic devices play an important role on the performance of the Formula SAE racing car. The rear wing is the most significant and popular element, which offers primary down force and optimizes the wake. In traditional rear wing optimization, the optimization variables are first selected, and separately enumerated according to the analyzing experience of the racing car’s external flow field, and thus the optimal design is chosen by comparison. This method is complicated, and even might lose some key sample points. In this paper, the attack angle of the rear wing and the relative position parameters are set as design variables; then the design variables’ combination is determined by the DOE experimental design method. The aerodynamic lift and drag of the racing car for these variables’ combinations are obtained by the computational fluid dynamics method. With these sample points, the approximation model is produced by the response surface method. For the sake of gaining the best lift to drag ( FL/ FD) ratio, i.e. maximum down force and the minimum drag force, the optimal solution is found by the genetic algorithm. The result shows that the established optimization procedure can optimize the rear wing’s aerodynamic characteristic on the racing car effectively and have application values in the practical engineering.

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.

Optimization Design of Cantilever Supporting Structure

2013 ◽  
Vol 357-360 ◽  
pp. 2410-2413
Author(s):  
Wei Xu ◽  
Jian Sheng Feng ◽  
Fei Fei Feng
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Design Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Original Design ◽  
Supporting Structure ◽  
Primary Object ◽  
Design Variables ◽  
Optimum Solution

The primary object of this fundamental research is to reveal the application of genetic algorithm improved on the optimization design of cantilever supporting structure. In order to meet the strength of pile body and pile top displacement as well as design variables subjected to constraint, an algorithm is carried on to seek the optimum solution and relevant examples by means of comprehensively considering the effects on center-to-center spacing between piles,pile diameter and quantity of distributed steel, which is taken the lowest engineering cost as objective function. Through the comparison of the optimized scheme and original design, this fruitful work provides explanation to the effectiveness of genetic algorithm in optimization design. These findings of the research lead to the conclusion that the shortcomings of traditional design method is easy to fall into local optimal solution. The new optimization method can overcome this drawback.

Ship Cabin Layout Optimization Design Based on the Improved Genetic Algorithm Method

2013 ◽  
Vol 300-301 ◽  
pp. 146-149 ◽  
Author(s):  
Yun Long Wang ◽  
Chen Wang ◽  
Yan Lin
Keyword(s):  
Genetic Algorithm ◽  
Optimization Design ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
The Other ◽  
Improved Genetic Algorithm ◽  
Local Optimal Solution ◽  
Genetic Algorithm Method ◽  
Np Hard Problem ◽  
The One

Based on the improved genetic algorithm method, a kind of the optimization techniques to solve the problem about the ship cabin layout is presented. The problem about the ship cabin layout is a NP-hard problem. This article has used the genetic algorithm method to solve it .However, for the simple and easy procedure, the basic genetic algorithm is slow and easy to fall into a local optimal solution. Therefore, it must be improved. This article has made the following two improvements: on the one hand using the niche method to solve the multi- peak problem; on the other hand using the climbing method to solve the slow and premature convergence. The simulation tests show that this approach proposed by authors is feasible and valid and the result is satisfied.

Multi-Objective Optimization Calibration of Spark Advanced Angle Based on NSGA-II Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 1959-1962
Author(s):  
Hao Ba ◽  
Bao Mei Qiu ◽  
Pei Pei Chen
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Gasoline Engine ◽  
Optimal Solution ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Improved Genetic Algorithm ◽  
Nsga Ii ◽  
Multi Objective ◽  
Optimal Solution Set

Modern gasoline engine spark advanced angle calibration is a multi-objective optimization problem, commonly used genetic algorithm to solve this problem. However, the traditional genetic algorithm tends to local optimum probability of a larger, easy to fall into premature, this defect is likely to cause the solution is not the optimal solution set. To address this issue, the non-dominated sorting genetic algorithm II for the spark advanced angle optimization, through crowding distance maintain the diversity, overcome super individuals overgrowth, improved genetic algorithm post search results. Experimental results show the effectiveness of this method.

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.

scholarly journals An Innovative Optimization Design Procedure for Mechatronic Systems with a Multi-Criteria Formulation

2021 ◽  
Vol 11 (19) ◽  
pp. 8900
Author(s):  
Cuauhtémoc Morales-Cruz ◽  
Marco Ceccarelli ◽  
Edgar Alfredo Portilla-Flores
Keyword(s):  
Optimization Design ◽  
Optimization Problem ◽  
Heuristic Algorithms ◽  
Design Procedure ◽  
Concurrent Design ◽  
Mechatronic Systems ◽  
Design Variables ◽  
One Step ◽  
Task Oriented ◽  
And Task

This paper presents an innovative Mechatronic Concurrent Design procedure to address multidisciplinary issues in Mechatronics systems that can concurrently include traditional and new aspects. This approach considers multiple criteria and design variables such as mechanical aspects, control issues, and task-oriented features to formulate a concurrent design optimization problem that is solved using but not limited to heuristic algorithms. Furthermore, as an innovation, this procedure address all considered aspects in one step instead of multiple sequential stages. Finally, this work discusses an example referring to Mechatronic Design to show the procedure performed and the results show its capability.

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