Modified Genetic Algorithm based on A* Algorithm of Multi Objective Optimization for Path Planning

2014 ◽  
Vol 2 (4) ◽  
pp. 357-362 ◽  
Author(s):  
Bashra K. Oleiwi ◽  
Hubert Roth ◽  
Bahaa I. Kazem
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Multi Objective Optimization ◽  
A Algorithm ◽  
Multi Objective ◽  
Modified Genetic Algorithm

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.

MULTI-OBJECTIVE OPTIMIZATION OF OFFSHORE TSUNAMI OBSERVATION POINTS BY GENETIC ALGORITHM

2020 ◽  
Vol 76 (2) ◽  
pp. I_325-I_330
Author(s):  
Kazutoshi KURAMOTO ◽  
Fumiyasu MAKINOSHIMA ◽  
Anawat SUPPASRI ◽  
Fumihiko IMAMURA
Keyword(s):  
Genetic Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective

Multi-objective optimization of injection-molded plastic parts using entropy weight, random forest, and genetic algorithm methods

2020 ◽  
Vol 40 (4) ◽  
pp. 360-371
Author(s):  
Yanli Cao ◽  
Xiying Fan ◽  
Yonghuan Guo ◽  
Sai Li ◽  
Haiyue Huang
Keyword(s):  
Genetic Algorithm ◽  
Random Forest ◽  
Regression Model ◽  
Process Parameters ◽  
Multi Objective Optimization ◽  
Entropy Weight ◽  
Multi Objective ◽  
Injection Molded ◽  
Plastic Parts

AbstractThe qualities of injection-molded parts are affected by process parameters. Warpage and volume shrinkage are two typical defects. Moreover, insufficient or excessively large clamping force also affects the quality of parts and the cost of the process. An experiment based on the orthogonal design was conducted to minimize the above defects. Moldflow software was used to simulate the injection process of each experiment. The entropy weight was used to determine the weight of each index, the comprehensive evaluation value was calculated, and multi-objective optimization was transformed into single-objective optimization. A regression model was established by the random forest (RF) algorithm. To further illustrate the reliability and accuracy of the model, back-propagation neural network and kriging models were taken as comparative algorithms. The results showed that the error of RF was the smallest and its performance was the best. Finally, genetic algorithm was used to search for the minimum of the regression model established by RF. The optimal parameters were found to improve the quality of plastic parts and reduce the energy consumption. The plastic parts manufactured by the optimal process parameters showed good quality and met the requirements of production.

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