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.

An Effective Community Detection Method Based on Improved Genetic Algorithm

2014 ◽  
Vol 568-570 ◽  
pp. 852-857
Author(s):  
Lu Wang ◽  
Yong Quan Liang ◽  
Qi Jia Tian ◽  
Jie Yang ◽  
Chao Song ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Community Structure ◽  
Community Detection ◽  
Detection Method ◽  
Initial Population ◽  
Crossover Operator ◽  
Improved Genetic Algorithm ◽  
Mutation Process ◽  
Effective Community ◽  
New Community

Detecting community structure from complex networks has triggered considerable attention in several application domains. This paper proposes a new community detection method based on improved genetic algorithm (named CDIGA), which tries to find the best community structure by maximizing the network modularity. String encoding is used to realize genetic representation. Parts of nodes assign their community identifiers to all of their neighbors to ensure the convergence of the algorithm and eliminate unnecessary iterations when initial population is created. Crossover operator and mutation operator are improved too, one-way crossover strategy is introduced to crossover process, the Connect validity of mutation node is ensured in mutation process. We compared it with three other algorithms in computer generated networks and real world networks, Experiment Results show that the improved algorithm is highly effective for discovering community structure.

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.

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.

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