Optimization algorithms for site-directed protein recombination experiment planning.

2010 ◽  
Author(s):  
Wei. Zheng
Keyword(s):  
Optimization Algorithms ◽  
Experiment Planning ◽  
Protein Recombination

ИССЛЕДОВАНИЕ ТОЧНОСТИ ДЕТАЛЕЙ, ПОЛУЧАЕМЫХ ПРИ РАЗДЕЛИТЕЛЬНЫХ ОПЕРАЦИЯХ В ПЕРЕНАЛАЖИВАЕМЫХ ШТАМПАХ

2018 ◽  
pp. 52-63
Author(s):  
Е. А. Фролов ◽  
В. В. Агарков ◽  
С. И. Кравченко ◽  
С. Г. Ясько
Keyword(s):  
Sheet Metal ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Balance Method ◽  
Experiment Planning ◽  
Practical Recommendations

To determine the accuracy of the readjustable punches for separating operations (perforation + punching out) of sheet-metal forming, the accuracy parameters were analyzed using the random balance method using the method of experiment planning. Analytical dependencies are obtained to determine the values of deviation of the outer and inner contour dimensions of perforated and punched out sheet parts. From the dependencies obtained, it is possible to estimate and predict the value of deviation in the dimensions of the resulting part at any time during the operation of the punch. Practical recommendations on the calculation of the actuating dimensions of the working elements (stamping punch, matrix) of readjustable punches are offered.

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC’17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

Comparison of optimization algorithms for aerodynamic shape design

AIAA Journal ◽  
1997 ◽  
Vol 35 ◽  
pp. 1413-1415
Author(s):  
Shigeru Obayashi ◽  
Takanori Tsukahara
Keyword(s):  
Optimization Algorithms ◽  
Shape Design ◽  
Aerodynamic Shape
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