Solving Collaborative Manufacturing Resources Optimization Deployment Problems Based on Improved DNA Genetic Algorithm

2011 ◽  
Vol 128-129 ◽  
pp. 289-292
Author(s):  
Shu Zhi Nie ◽  
Yan Hua Zhong
Keyword(s):  
Genetic Algorithm ◽  
Ranking Method ◽  
Test Results ◽  
Improved Genetic Algorithm ◽  
Scaling Method ◽  
Dna Computation ◽  
Algorithm Performance ◽  
Collaborative Manufacturing ◽  
Manufacturing Resources ◽  
Conventional Genetic Algorithm

In this paper, According to the collaborative manufacturing resources optimization deployment problems, designed subsection crossover and subsection mutation based on process code, adopted fitness scaling method and ranking method to select operators, proposed an improved genetic algorithm based on DNA computation for solving the resources optimization deployment problems, so that the offspring are better able to inherit the good features of parent. Through simulation, tested the designed algorithm performance; by comparing with conventional genetic algorithm test results, it proved the validity of the designed algorithm.

An Improved Hybrid Genetic Algorithm and Performance Study

2012 ◽  
Vol 482-484 ◽  
pp. 95-98
Author(s):  
Wei Dong Ji ◽  
Ke Qi Wang
Keyword(s):  
Genetic Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Optimization Method ◽  
Convergence Speed ◽  
Test Results ◽  
Improved Genetic Algorithm ◽  
Performance Study ◽  
Speed Up ◽  
And Performance

Put forward a kind of the hybrid improved genetic algorithm of particle swarm optimization method (PSO) combine with and BFGS algorithm of, this method using PSO good global optimization ability and the overall convergence of BFGS algorithm to overcome the blemish of in the conventional algorithm slow convergence speed and precocious and local convergence and so on. Through the three typical high dimensional function test results show that this method not only improved the algorithm of the global search ability, to speed up the convergence speed, but also improve the quality of the solution and its reliability of optimization results.

Solving Steel-Making and Continuous-Casting Scheduling Problem with Fuzzy Processing and Delivery Time Using Genetic Algorithm

2010 ◽  
Vol 139-141 ◽  
pp. 1679-1683 ◽  
Author(s):  
Hong Bing Wang ◽  
Ai Jun Xu ◽  
Dong Feng He
Keyword(s):  
Genetic Algorithm ◽  
Continuous Casting ◽  
Production Scheduling ◽  
Scheduling Problem ◽  
Test Results ◽  
Delivery Time ◽  
Crossover Operator ◽  
Improved Genetic Algorithm ◽  
Steel Making ◽  
Early Maturing

The real production scheduling problem between steel-making and continuous-casting can be modeled as JSSP with fuzzy processing and delivery time. An improved genetic algorithm is proposed for solving this problem and the improved aspects include the mechanism for preventing early-maturing and the job filter order-based crossover operator. The test results show that the improved genetic algorithm can find better solutions than other three algorithms. A real production scheduling problem of steel-making and continuous-casting is computed using the improved genetic algorithm and it shows the algorithm is effective.

AGV Scheduling Optimizing Research of Collaborative Manufacturing System Based on Improved Genetic Algorithm

2013 ◽  
Vol 300-301 ◽  
pp. 55-61 ◽  
Author(s):  
Nai Fei Ren ◽  
Dan Liu ◽  
Yan Zhao ◽  
Xiao Bing Ge
Keyword(s):  
Genetic Algorithm ◽  
Manufacturing System ◽  
Simulation Experiment ◽  
Improved Genetic Algorithm ◽  
Manufacture System ◽  
Collaborative Manufacturing ◽  
Coding Method ◽  
Crossover Method ◽  
Standard Genetic Algorithm ◽  
Improved Algorithm

This paper provides improved genetic algorithm to solve productivity efficiency in Collaborative Manufacture System (CMS) according to its own characteristics.This improved algorithm not only improved coding method but also improved crossover method and mutation method.And the simulation experiment result in CMS validated the productivity efficiency promoted compared with improved and standard genetic algorithm.

scholarly journals Research on Resource Scheduling of Cloud Computing Based on Improved Genetic Algorithm

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Juanzhi Zhang ◽  
Fuli Xiong ◽  
Zhongxing Duan
Keyword(s):  
Genetic Algorithm ◽  
Data Center ◽  
Completion Time ◽  
Resource Scheduling ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Cloud Data Center ◽  
Two Stage ◽  
Cloud Data ◽  
Conventional Genetic Algorithm

In order to solve the problem that the resource scheduling time of cloud data center is too long, this paper analyzes the two-stage resource scheduling mechanism of cloud data center. Aiming at the minimum task completion time, a mathematical model of resource scheduling in cloud data center is established. The two-stage resource scheduling optimization simulation is realized by using the conventional genetic algorithm. On the technology of the conventional genetic algorithm, an adaptive transformation operator is designed to improve the crossover and mutation of the genetic algorithm. The experimental results show that the improved genetic algorithm can significantly reduce the total completion time of the task, and has good convergence and global optimization ability.

Online Self-Test System Grouping Function of Genetic Algorithm-Based Optimization

2013 ◽  
Vol 380-384 ◽  
pp. 2776-2780
Author(s):  
Li Ping Zhang ◽  
Da Shen Xue
Keyword(s):  
Genetic Algorithm ◽  
Test System ◽  
Testing System ◽  
Test Paper ◽  
Improved Genetic Algorithm ◽  
Test Characteristics ◽  
Self Testing ◽  
Self Test ◽  
Conventional Genetic Algorithm

The paper mainly discusses the genetic algorithm to optimize the test paper module and Research and Implementation of online self-test system. Online self-test characteristics of the system, coding, crossover and mutation improvement on traditional genetic algorithm. Experimental results show that, the improved genetic algorithm has better performance than the conventional genetic algorithm, improves the efficiency of solving the quality of test paper and issues, promotes a more widely used online self-testing system in the field of education.

Bi-Objective Power Dispatch on Micro-Grid System Using Improved Genetic Algorithms

2012 ◽  
Vol 614-615 ◽  
pp. 1738-1743
Author(s):  
Fei Hu Hu ◽  
Ji Ze Zhang ◽  
Bei Long Ma ◽  
Lu Lu Liu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Real Number ◽  
Production Cost ◽  
Test Results ◽  
Grid System ◽  
Fuel Cost ◽  
Improved Genetic Algorithm ◽  
Power Dispatch ◽  
Micro Grid

In this paper, a micro-grid power dispatch network is built up, and a real-number-coded genetic algorithm is adapted and proposed to solve the bi-objective dispatch problem, which is to minimize both fuel cost and production cost simultaneously. By using hybrid factors, we turn the two objectives into a single one, and then solve it by the improved genetic algorithm. Test results show the efficiency of the proposed algorithm.

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