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.

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.

Multi-Objective Optimization of Manufacturing Workshop Layout Based on Improved Genetic Algorithm

2013 ◽  
Vol 860-863 ◽  
pp. 2664-2668
Author(s):  
Bi Hong Tang ◽  
Zhi Xia Zhang
Keyword(s):  
Genetic Algorithm ◽  
Mutation Rate ◽  
Facility Layout ◽  
Adaptive Genetic Algorithm ◽  
Multi Objective Optimization ◽  
Improved Genetic Algorithm ◽  
Multi Objective ◽  
Crossover Rate ◽  
Mutation Strategy ◽  
Adaptive Crossover

A good manufacturing workshop layout can influence the profit of the manufacturing enterprises after the product coming on stream. Facility layout of workshop is a combinational optimization problem. The multi-objective optimization model which integrates the available problem of facility layout of workshop is established. Adaptive Genetic Algorithm is presented because of the disadvantage of simple Genetic Algorithm in solving this model. This algorithm use the adaptive crossover and mutation strategy which is used to nonlinear processing for crossover rate and mutation rate, then crossover rate and mutation rate are changed with the colony adaptation degree of each generation. It has some advantage, such as higher search speed, higher convergence precision, and so on. Finally an example is used to show the effectiveness of the method.

A New Path Planning Method of Robot Based on Improved Adaptive Genetic Algorithms

2013 ◽  
Vol 278-280 ◽  
pp. 590-593
Author(s):  
Huai Qiang Li ◽  
Ming Xia Shi
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Path Planning ◽  
Simulation Experiment ◽  
Planning Method ◽  
Adaptive Genetic Algorithm ◽  
Robot Path Planning ◽  
Adaptive Genetic Algorithms ◽  
Planning Space ◽  
Robot Path

This paper presents a new method of robot path planning which is based on improved adaptive genetic algorithm. On the foundation of building the model in planning space by link-graph, we first gets the feasible paths by using Ford algorithms ,and then adjusts the points of every path by using improved adaptive genetic algorithms to get the best or better path. The simulation experiment shows the advancement of the method.

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.

Optimization Design of Structure and Parameters for RBF Neural Network Using Hybrid Hierarchy Genetic Algorithm

2014 ◽  
Vol 951 ◽  
pp. 274-277 ◽  
Author(s):  
Xu Sheng Gan ◽  
Can Yang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimization Design ◽  
Rbf Neural Network ◽  
Hybrid Genetic Algorithm ◽  
Proposed Model ◽  
Crossover Probability ◽  
Ga Algorithm ◽  
The Hierarchical Structure ◽  
Adaptive Crossover

To improve the optimization design of Radial Basis Function (RBF) neural network, a RBF neural network based on a hybrid Genetic Algorithm (GA) is proposed. First the hierarchical structure and adaptive crossover probability is introduced into the traditional GA algorithm for the improvement, and then the hybrid GA algorithm is used to optimize the structure and parameters of the network. The simulation indicates that the proposed model has a good modeling performance.

scholarly journals An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Yunqing Rao ◽  
Dezhong Qi ◽  
Jinling Li
Keyword(s):  
Genetic Algorithm ◽  
Parallel Machines ◽  
Convergence Rates ◽  
Cutting Stock ◽  
Hierarchical Coding ◽  
Crossover Probability ◽  
Speed Up ◽  
Adaptive Crossover ◽  
Process Constraints ◽  
Hierarchical Genetic Algorithm

For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involvesncutting patterns formnon-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

QoS Routing Optimization Based on Improved Genetic Algorithm

2014 ◽  
Vol 998-999 ◽  
pp. 1169-1173
Author(s):  
Chang Lin He ◽  
Yu Fen Li ◽  
Lei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Computational Complexity ◽  
Fitness Function ◽  
Qos Routing ◽  
Experimental Results ◽  
Improved Genetic Algorithm ◽  
Coding Schemes ◽  
Routing Optimization

A improved genetic algorithm is proposed to QoS routing optimization. By improving coding schemes, fitness function designs, selection schemes, crossover schemes and variations, the proposed method can effectively reduce computational complexity and improve coding accuracy. Simulations are carried out to compare our algorithm with the traditional genetic algorithms. Experimental results show that our algorithm converges quickly and is reliable. Hence, our method vastly outperforms the traditional algorithms.

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.

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.

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