Multiobjective Optimization Design of a Hybrid Actuator with Genetic Algorithm

2006 ◽  
pp. 845-855
Author(s):  
Ke Zhang
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Optimization Design ◽  
Hybrid Actuator

scholarly journals Multiobjective optimization design of the solar field and reverse osmosis system with preheating feed water using Genetic algorithm

2018 ◽  
Vol 6 (6) ◽  
pp. 624-642 ◽  
Author(s):  
Iman Ebrahimi Ghoujdi ◽  
Hasti Hadiannasab ◽  
Mokhtar Bidi ◽  
Abbas Naeimi ◽  
Mohammad Hossein Ahmadi ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Reverse Osmosis ◽  
Optimization Design ◽  
Feed Water ◽  
Solar Field

scholarly journals Multiobjective Optimization Design of Spinal Pedicle Screws Using Neural Networks and Genetic Algorithm: Mathematical Models and Mechanical Validation

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yongyut Amaritsakul ◽  
Ching-Kong Chao ◽  
Jinn Lin
Keyword(s):  
Genetic Algorithm ◽  
Multiobjective Optimization ◽  
Optimization Design ◽  
Bending Strength ◽  
Three Dimensional ◽  
Pedicle Screws ◽  
Experimental Tests ◽  
Pullout Strength ◽  
Pareto Optima ◽  
Optimization Study

Short-segment instrumentation for spine fractures is threatened byrelatively highfailure rates. Failure of the spinal pedicle screws including breakage and loosening may jeopardize the fixation integrity and lead to treatment failure. Two important design objectives, bending strength and pullout strength, may conflict with each other and warrant a multiobjective optimization study. In the present study using the three-dimensional finite element (FE) analytical results based on an L25orthogonal array, bending and pullout objective functions were developed by an artificial neural network (ANN) algorithm, and the trade-off solutions known as Pareto optima were explored by a genetic algorithm (GA). The results showed that the knee solutions of the Pareto fronts with both high bending and pullout strength ranged from 92% to 94% of their maxima, respectively. In mechanical validation, the results of mathematical analyses were closely related to those of experimental tests with a correlation coefficient of −0.91 for bending and 0.93 for pullout (P<0.01for both). The optimal design had significantly higher fatigue life (P<0.01) and comparable pullout strength as compared with commercial screws. Multiobjective optimization study of spinal pedicle screws using the hybrid of ANN and GA could achieve an ideal with high bending and pullout performances simultaneously.

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