Optimal Design of Control Parameters for Extended Range Munitions Using Improved Genetic Algorithm

2013 ◽  
Vol 709 ◽  
pp. 611-615
Author(s):  
Si Jiang Chang ◽  
Qi Chen
Keyword(s):  
Genetic Algorithm ◽  
Pitch Angle ◽  
Adaptive Genetic Algorithm ◽  
Control Parameters ◽  
Improved Genetic Algorithm ◽  
Optimal Model ◽  
Control Effect ◽  
Optimal Method ◽  
Extended Range ◽  
Simulation Results

To obtain the best control effect for the controller of Extended Range Munitions (ERM), an optimal method for control parameters design was proposed. The adaptive genetic algorithm (GA) with real coding and the elites to keep the tactics were combined, based on which the original GA was improved. An optimal model of pitch angle controller for a type of ERM was established and the improved GA was used as the solver. Taking the stabilization loop as an example, the Powell algorithm, the simple GA and the improved GA were used to optimization, respectively. The simulation results indicate that the improved GA is more efficient and possesses stronger capability for searching.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

scholarly journals Integrated Tomato Picking and Distribution Scheduling Based on Maturity

2020 ◽  
Vol 12 (19) ◽  
pp. 7934
Author(s):  
Anqi Zhu ◽  
Bei Bian ◽  
Yiping Jiang ◽  
Jiaxiang Hu
Keyword(s):  
Genetic Algorithm ◽  
Time Windows ◽  
Joint Optimization ◽  
Adaptive Genetic Algorithm ◽  
Maturity Model ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Order Processing ◽  
Supply Chain Efficiency ◽  
Distribution Scheduling

Agriproducts have the characteristics of short lifespan and quality decay due to the maturity factor. With the development of e-commerce, high timelines and quality have become a new pursuit for agriproduct online retailing. To satisfy the new demands of customers, reducing the time from receiving orders to distribution and improving agriproduct quality are significantly needed advancements. In this study, we focus on the joint optimization of the fulfillment of online tomato orders that integrates picking and distribution simultaneously within the context of the farm-to-door model. A tomato maturity model with a firmness indicator is proposed firstly. Then, we incorporate the tomato maturity model function into the integrated picking and distribution schedule and formulate a multiple-vehicle routing problem with time windows. Next, to solve the model, an improved genetic algorithm (the sweep-adaptive genetic algorithm, S-AGA) is addressed. Finally, we prove the validity of the proposed model and the superiority of S-AGA with different numerical experiments. The results show that significant improvements are obtained in the overall tomato supply chain efficiency and quality. For instance, tomato quality and customer satisfaction increased by 5% when considering the joint optimization, and the order processing speed increased over 90% compared with traditional GA. This study could provide scientific tomato picking and distribution scheduling to satisfy the multiple requirements of consumers and improve agricultural and logistics sustainability.

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.

Intelligent Generating Examination Paper from Item Bank Based on Improved Genetic Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 3637-3640
Author(s):  
Li Feng ◽  
Qian Wu ◽  
Jing Shao Zhang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Computational Study ◽  
Item Bank ◽  
Test Paper ◽  
Improved Genetic Algorithm ◽  
Fitness Evaluation ◽  
Simulation Results ◽  
The Mathematical Model ◽  
Examination Paper

In this paper, we analyze the disadvantage of common generating test paper algorithm. An improved genetic algorithm (IGA) is proposed and used in auto-generating examination paper algorithm. We design the mathematical model of auto-generating test paper algorithm and improved the traditional GA fitness evaluation form. A computational study is carried out to verify the algorithm. Simulation results demonstrate that the performance of IGA can work efficiently than traditional ones.

The Study of Optimal Fuzzy PID-Smith Control Based on Genetic Algorithm

2013 ◽  
Vol 313-314 ◽  
pp. 448-452
Author(s):  
Dian Ting Liu ◽  
Hai Xia Li
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Control Method ◽  
Order System ◽  
Membership Functions ◽  
Fuzzy Pid ◽  
Improved Genetic Algorithm ◽  
Simulation Results ◽  
The Stability ◽  
Better Than

In this paper, the improved genetic algorithm is applied to optimize the quantization factors and the scaling factors of fuzzy control, and the optimized rule table and membership functions is obtained according to certain performances. Then a kind of optimal fuzzy PID-Smith control method based on genetic algorithm is proposed and its simulation model is built in this paper, a second-order system is simulated and analyzed. The results show that requirements of deterministic performances of the new control method are better than the conventional methods through the simulation results in the stability, rapidity and robustness.

Resource Optimization Based on Adaptive Genetic Algorithm

2013 ◽  
Vol 339 ◽  
pp. 784-788
Author(s):  
Lei Wang ◽  
Yu Yun Kang
Keyword(s):  
Genetic Algorithm ◽  
Task Allocation ◽  
Optimization Problem ◽  
Resource Optimization ◽  
Adaptive Genetic Algorithm ◽  
Manufacturing Environment ◽  
Simulation Results

In order to allocate tasks and optimize resources well in dynamical manufacturing environment, the model for task allocation is established. An adaptive genetic algorithm (AGA) is applied to deal with it. A machine-based encoding approach is also adopted. The simulation results testify the validity of this method, and therefore the task allocation and resources optimization problem could be dealt with efficiently.

The Attacking Dispersion Optimization in Multi Launch Rocket System Base-On Improved Genetic Algorithm

2014 ◽  
Vol 670-671 ◽  
pp. 1499-1502
Author(s):  
Wei Wang ◽  
Wei Dong Chen ◽  
Shu Qiang Zhang ◽  
Jiang Long Li ◽  
Ya En Xie
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Fitness Function ◽  
Reference Value ◽  
Weapon System ◽  
Improved Genetic Algorithm ◽  
Optimal Model ◽  
Overall Design ◽  
Mixed Coding ◽  
Rocket System

Firing dispersion of multi-launch rocket system is affected by launch sequence and firing interval significantly. Firing order and firing interval of the existing multi launch rocket system (MLRS) are optimized to improve the firing performance of the existing weapon system without changing the overall design of the weapon system. On one hand, based on optimization problem, the firing dispersion optimal model is established and the genetic algorithm is improved therefore, a sequence of mixed coding genetic algorithm is designed. On the other hand, simulation optimization of firing dispersion has been finished by the aid of fitness function which is based on the optimal model. Meanwhile, it testifies this algorithm’s validity and the simulation results can provide a certain reference value for engineering experiment.

Solving Job-Shop Scheduling Problem by an Improved Genetic Algorithm

2011 ◽  
Vol 411 ◽  
pp. 588-591
Author(s):  
Yan Li Yang ◽  
Wei Wei Ke
Keyword(s):  
Genetic Algorithm ◽  
Convergence Rate ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Local Optimum ◽  
Job Shop Scheduling Problem ◽  
Improved Genetic Algorithm ◽  
Shop Scheduling ◽  
Global Optimal ◽  
Simulation Results

An improved genetic algorithm is proposed by introducing selection operation and crossover operation, which overcomes the limitations of the traditional genetic algorithm, avoids the local optimum, improves its convergence rate and the diversity of population, and solves the problems of population prematurity and slow convergence rate in the basic genetic algorithm. Simulation results show that compared with other improved genetic algorithms, the proposed algorithm is better in finding global optimal and convergent rate.

scholarly journals Research on the Application of Adaptive Genetic Algorithm in Software Unit Security Test Simulation

2021 ◽  
Vol 2066 (1) ◽  
pp. 012010
Author(s):  
Xuefeng Ge
Keyword(s):  
Genetic Algorithm ◽  
Control Structure ◽  
Software Process ◽  
Adaptive Genetic Algorithm ◽  
Model Control ◽  
Simulation Results ◽  
Security Test

Abstract At present, the security test and simulation of software unit mainly focuses on several links, such as software control structure amelioration, software process alternating quantity model control and model inspection tech, and there are still many shortcomings, such as high missed inspection rate, difficult to effectively guarantee the needs of practice, etc. Based on this, this paper first analyses the purpose and principle of software unit security test and simulation, then studies the utilization of ameliorated genetic algorithm in software unit security test simulation, and finally gives the simulation results analysis of software unit security test based on AGA.

State recognition method for machining process of a large spot welder based on improved genetic algorithm and hidden Markov model

2016 ◽  
Vol 231 (11) ◽  
pp. 2135-2146 ◽  
Author(s):  
Bing Wang ◽  
Ping Yan ◽  
Qiang Zhou ◽  
Libing Feng
Keyword(s):  
Genetic Algorithm ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Machining Process ◽  
Adaptive Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
Recognition Method ◽  
State Recognition ◽  
Model Recognition

Large spot welder is an important equipment in rail transit equipment manufacturing industry, but having the problem of low utilization rate and low effectlvely machining rate. State monitoring can master its operating states real time and comprehensively, and providing data support for state recognition. Hidden Markov model is a state classification method, but it is sensitive to the initial model parameters and easy to trap into a local optima. Genetic algorithm is a global searching method; however, it is quite poor at hill climbing and also has the problem of premature convergence. In this paper, proposing the improved genetic algorithm, and combining improved genetic algorithm and hidden Markov model, a new method of state recognition method named improved genetic algorithm–hidden Markov model is proposed. In the proposed method, improved genetic algorithm is used for optimizing the initial parameters, and hidden Markov model as a classifier to recognize the operating states for machining process. This method is also compared with the other two recognition methods named adaptive genetic algorithm–hidden Markov model and hidden Markov model, in which adaptive genetic algorithm is similarly used for optimizing the initial parameters, however hidden Markov model (in both methods) as a classifier. Experimental results show that the proposed method is very effective, and the improved genetic algorithm–hidden Markov model recognition method is superior to the adaptive genetic algorithm–hidden Markov model and hidden Markov model recognition method.

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