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.

Multi-Objective Optimization Calibration of Spark Advanced Angle Based on NSGA-II Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 1959-1962
Author(s):  
Hao Ba ◽  
Bao Mei Qiu ◽  
Pei Pei Chen
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Gasoline Engine ◽  
Optimal Solution ◽  
Local Optimum ◽  
Multi Objective Optimization ◽  
Improved Genetic Algorithm ◽  
Nsga Ii ◽  
Multi Objective ◽  
Optimal Solution Set

Modern gasoline engine spark advanced angle calibration is a multi-objective optimization problem, commonly used genetic algorithm to solve this problem. However, the traditional genetic algorithm tends to local optimum probability of a larger, easy to fall into premature, this defect is likely to cause the solution is not the optimal solution set. To address this issue, the non-dominated sorting genetic algorithm II for the spark advanced angle optimization, through crowding distance maintain the diversity, overcome super individuals overgrowth, improved genetic algorithm post search results. Experimental results show the effectiveness of this method.

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