A Vision Alignment Method Based on Improved Genetic Algorithm for IC Packaging

2011 ◽  
Vol 189-193 ◽  
pp. 4177-4181 ◽  
Author(s):  
Shi Wei Liu ◽  
Jun Lan Li ◽  
Xing Yu Zhao ◽  
Da Wei Zhang
Keyword(s):  
Genetic Algorithm ◽  
Adaptive Genetic Algorithm ◽  
Moment Invariants ◽  
Improved Genetic Algorithm ◽  
Ic Packaging ◽  
Otsu Method ◽  
Vision Alignment ◽  
The Moment ◽  
Positioning Time ◽  
Speed And Accuracy

In order to improve the speed and accuracy of vision alignment for IC packaging, genetic algorithm and Otsu method are applied to vision alignment. According to the features of the image in IC packaging, an improved self-adaptive genetic algorithm combined with Otsu method is proposed in this paper, and the moment invariants method is used to carry out the remaining steps of vision alignment. Finally, experiments are undertaken by using a kind of IC chip, results show that the positioning error is less than 2μm, and the positioning time is less than 60ms.

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.

Satellite Module Layout Design Based on Adaptive Bee Evolutionary Genetic Algorithm

2014 ◽  
Vol 538 ◽  
pp. 193-197
Author(s):  
Jian Jiang Su ◽  
Chao Che ◽  
Qiang Zhang ◽  
Xiao Peng Wei
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Layout Design ◽  
Adaptive Genetic Algorithm ◽  
Local Optimality ◽  
Mutation Probability ◽  
Evolutionary Genetic ◽  
Local Optimal Solution ◽  
The Moment ◽  
Crossover And Mutation

The main problems for Genetic Algorithm (GA) to deal with the complex layout design of satellite module lie in easily trapping into local optimality and large amount of consuming time. To solve these problems, the Bee Evolutionary Genetic Algorithm (BEGA) and the adaptive genetic algorithm (AGA) are introduced. The crossover operation of BEGA algorithm effectively reinforces the information exploitation of the genetic algorithm, and introducing random individuals in BEGA enhance the exploration capability and avoid the premature convergence of BEGA. These two features enable to accelerate the evolution of the algorithm and maintain excellent solutions. At the same time, AGA is adopted to improve the crossover and mutation probability, which enhances the escaping capability from local optimal solution. Finally, satellite module layout design based on Adaptive Bee Evolutionary Genetic Algorithm (ABEGA) is proposed. Numerical experiments of the satellite module layout optimization show that: ABEGA outperforms SGA and AGA in terms of the overall layout scheme, enveloping circle radius, the moment of inertia and success rate.

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.

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.

scholarly journals An Improved Adaptive Genetic Algorithm for Image Segmentation and Vision Alignment Used in Microelectronic Bonding

2014 ◽  
Vol 19 (3) ◽  
pp. 916-923 ◽  
Author(s):  
Fujun Wang ◽  
Junlan Li ◽  
Shiwei Liu ◽  
Xingyu Zhao ◽  
Dawei Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Adaptive Genetic Algorithm ◽  
Vision Alignment

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.

Image Segmentation Method Based on Improved Genetic Algorithm and Fuzzy Clustering

2010 ◽  
Vol 143-144 ◽  
pp. 379-383 ◽  
Author(s):  
Jing Zhang ◽  
Xiang Zhang ◽  
Jie Zhang
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Cluster Center ◽  
Adaptive Genetic Algorithm ◽  
Segmentation Method ◽  
Improved Genetic Algorithm ◽  
Advantages And Disadvantages ◽  
Initial Cluster ◽  
Important Means

Image segmentation is an important means of the implementation of image analysis. The existing segmentation methods have their own advantages and disadvantages in segmentation time and segmentation effect. Image segmentation based on fuzzy clustering and genetic algorithm is studied. An adaptive genetic algorithm is improved, the crossover rate and mutation rate are optimized, and a new adaptive operator is adopted to achieve a non-linear adaptive adjustment. A new combined image segmentation means is presented, in which the genetic algorithm is adopted to optimize the initial cluster center and then the fuzzy clustering is used for image segmentation. The practice proves that this image segmentation method and algorithm is superior to the traditional one, which improves the segmentation performance and the segmentation effect.

scholarly journals Applying a New Adaptive Genetic Algorithm to Study the Layout of Drilling Equipment in Semisubmersible Drilling Platforms

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Wensheng Xiao ◽  
Lei Wu ◽  
Xue Tian ◽  
Jingli Wang
Keyword(s):  
Genetic Algorithm ◽  
Adaptive Methods ◽  
Selection Method ◽  
High Rate ◽  
Adaptive Genetic Algorithm ◽  
Improved Genetic Algorithm ◽  
Effective Population ◽  
Algorithm Selection ◽  
Genetically Manipulated ◽  
Crossover And Mutation

This study proposes a new selection method called trisection population for genetic algorithm selection operations. In this new algorithm, the highest fitness of 2N/3 parent individuals is genetically manipulated to reproduce offspring. This selection method ensures a high rate of effective population evolution and overcomes the tendency of population to fall into local optimal solutions. Rastrigin’s test function was selected to verify the superiority of the method. Based on characteristics of arc tangent function, a genetic algorithm crossover and mutation probability adaptive methods were proposed. This allows individuals close to the average fitness to be operated with a greater probability of crossover and mutation, while individuals close to the maximum fitness are not easily destroyed. This study also analyzed the equipment layout constraints and objective functions of deep-water semisubmersible drilling platforms. The improved genetic algorithm was used to solve the layout plan. Optimization results demonstrate the effectiveness of the improved algorithm and the fit of layout plans.

Weld Seam Edge Detection Based 2D Maximum between-Cluster Variance and Genetic Algorithm

2014 ◽  
Vol 608-609 ◽  
pp. 3-6 ◽  
Author(s):  
Xiang Ying Dang
Keyword(s):  
Genetic Algorithm ◽  
Edge Detection ◽  
Time Complexity ◽  
Weld Seam ◽  
Visual Image ◽  
Improved Genetic Algorithm ◽  
Otsu Method ◽  
Welding Image ◽  
Image Edge

For welding image after pretreatment, First, GAP predictor templates predict error image,using the 2D Maximum Between· cluster Variance (OTSU) method combined with improved genetic algorithm to calculate the threshold ,Then classified errors image edge, weld image edge was extract. Experiments show that the method to detect the weld image edge not only decrease the time complexity, but also get the clearer edges, more details, and better visual image.

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