Mine-to-mill multi-objective optimal blending with technical and economic constraints using a modified genetic algorithm

2018 ◽  
Vol 9 (2) ◽  
pp. 109 ◽  
Author(s):  
Oscar Daniel Chuk ◽  
Carlos Gustavo Rodriguez Medina ◽  
Marina Romero ◽  
Luis Ventura Gutierrez ◽  
Juan Pedro Gil
Keyword(s):  
Genetic Algorithm ◽  
Multi Objective ◽  
Modified Genetic Algorithm ◽  
Economic Constraints ◽  
Optimal Blending

Mine-to-mill multi-objective optimal blending with technical and economic constraints using a modified genetic algorithm

2018 ◽  
Vol 9 (2) ◽  
pp. 109
Author(s):  
Juan Pedro Gil ◽  
Oscar Daniel Chuk ◽  
Carlos Gustavo Rodriguez Medina ◽  
Marina Romero ◽  
Luis Ventura Gutierrez
Keyword(s):  
Genetic Algorithm ◽  
Multi Objective ◽  
Modified Genetic Algorithm ◽  
Economic Constraints ◽  
Optimal Blending

scholarly journals Genetic Algorithm for the Multi-Objective Optimization of Product Gene

2012 ◽  
Vol 6-7 ◽  
pp. 116-121
Author(s):  
Qing Song Ai ◽  
Zhou Liu ◽  
Yan Wang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Engineering ◽  
Manufacturing Industry ◽  
Optimization Problems ◽  
Rapid Development ◽  
Optimal Solution ◽  
Adaptive Mutation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Modified Genetic Algorithm

In order to adapt to the rapid development of the manufacturing industry, product genetic engineering arises at the historic moment. Finding the optimal solution under more than one decision variables of the solution set is becoming the most important problems that we should solve. In this paper, we proposed a modified genetic algorithm to solve gene product genetic engineering of multi-objective optimization problems. The new concepts such as matrix encoding, column crossover and adaptive mutation are proposed as well. Experimental results show that the modified genetic algorithm can find the optimal solutions and match the customer’s expectations in modern manufacture.

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