An automatic genetic algorithm framework for the optimization of three-dimensional surgical plans of forearm corrective osteotomies

With the increase of the number of loaded goods, the number of optional loading schemes will increase exponentially. It is a long time and low efficiency to determine the loading scheme with experience. Genetic algorithm is a search heuristic algorithm used to solve optimization in the field of computer science artificial intelligence. Genetic algorithm can effectively select the optimal loading scheme but unable to utilize weight and volume capacity of cargo and truck. In this paper, we propose hybrid Genetic and fuzzy logic based cargo-loading decision making model that focus on achieving maximum profit with maximum utilization of weight and volume capacity of cargo and truck. In this paper, first of all, the components of the problem of goods stowage in the distribution center are analyzed systematically, which lays the foundation for the reasonable classification of the problem of goods stowage and the establishment of the mathematical model of the problem of goods stowage. Secondly, the paper abstracts and defines the problem of goods loading in distribution center, establishes the mathematical model for the optimization of single car three-dimensional goods loading, and designs the genetic algorithm for solving the model. Finally, Matlab is used to solve the optimization model of cargo loading, and the good performance of the algorithm is verified by an example. From the performance evaluation analysis, proposed the hybrid system achieve better outcomes than the standard SA model, GA method, and TS strategy.

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A three-dimensional simulation of discrete combustion of randomly dispersed micron-aluminum particle dust cloud and applying genetic algorithm to obtain the flame front

Energy ◽

10.1016/j.energy.2017.09.027 ◽

2017 ◽

Vol 140 ◽

pp. 804-817 ◽

Cited By ~ 7

Author(s):

Mehdi Bidabadi ◽

Mehdi Vahabzadeh Bozorg ◽

Vahid Bordbar

Keyword(s):

Genetic Algorithm ◽

Flame Front ◽

Aluminum Particle ◽

Dust Cloud ◽

Three Dimensional ◽

Dimensional Simulation

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A similarity score of protein three dimensional structures by hard ball model using a genetic algorithm (2): Overlapping of helix and sheet portions

Journal of Computer Aided Chemistry ◽

10.2751/jcac.3.117 ◽

2002 ◽

Vol 3 ◽

pp. 117-123

Author(s):

Shin-ichi Nakayama ◽

Eiichiro Mitsusada ◽

Peter Willett ◽

Masayuki Yoshida

Keyword(s):

Genetic Algorithm ◽

Three Dimensional ◽

Similarity Score ◽

Hard Ball

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Calibration of Kinematic Parameters of Robot Arm Using Laser Tracking System: Compensation for Non-Geometric Errors by Neural Networks and Selection of Optimal Measuring Points by Genetic Algorithm

International Journal of Automation Technology ◽

10.20965/ijat.2012.p0029 ◽

2012 ◽

Vol 6 (1) ◽

pp. 29-37 ◽

Cited By ~ 2

Author(s):

Seiji Aoyagi ◽

◽

Masato Suzuki ◽

Tomokazu Takahashi ◽

Jun Fujioka ◽

...

Keyword(s):

Genetic Algorithm ◽

Neural Networks ◽

Tracking System ◽

Kinematic Model ◽

Three Dimensional ◽

Geometric Parameters ◽

Geometric Errors ◽

Robot Arm ◽

Positioning Accuracy ◽

Laser Tracking

Offline teaching based on high positioning accuracy of a robot arm is desired to take the place of manual teaching. In offline teaching, joint angles are calculated using a kinematic model of the robot arm. However, a nominal kinematic model does not consider the errors arising in manufacturing or assembly, not to mention the non-geometric errors arising in gear transmission, arm compliance, etc. Therefore, a method of precisely calibrating the parameters in a kinematic model is required. For this purpose, it is necessary to measure the three-dimensional (3-D) absolute position of the tip of a robot arm. In this paper, a laser tracking system is employed as the measurement apparatus. The geometric parameters in the robot kinematic model are calibrated by minimizing errors between the measured positions and the predicted ones based on the model. The residual errors caused by non-geometric parameters are further reduced by using neural networks, realizing high positioning accuracy of sub-millimeter order. To speed up the calibration process, a smaller number of measuring points is preferable. Optimal measuring points, which realize high positioning accuracy while remaining small in number, are selected using Genetic Algorithm (GA).

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Two Comprehensive and Practical Methods for Simulating Pan Evaporation Under Different Climatic Conditions in Iran

Water ◽

10.3390/w13202814 ◽

2021 ◽

Vol 13 (20) ◽

pp. 2814

Author(s):

Mohammad Hassan Dehghanipour ◽

Hojat Karami ◽

Hamidreza Ghazvinian ◽

Zahra Kalantari ◽

Amir Hossein Dehghanipour

Keyword(s):

Genetic Algorithm ◽

Water Resource Management ◽

Three Dimensional ◽

Climatic Conditions ◽

Pan Evaporation ◽

Linear Quadratic ◽

Climate Type ◽

Method Error ◽

Basic Relationship ◽

Quadratic Relationship

Evaporation from surface water plays a crucial role in water accounting of basins, water resource management, and irrigation systems management. As such, the simulation of evaporation with high accuracy is very important. In this study, two methods for simulating pan evaporation under different climatic conditions in Iran were developed. In the first method, six experimental relationships (linear, quadratic, and cubic, with two input combinations) were determined for Iran’s six climate types, inspired by a multilayer perceptron neural network (MLP-NN) neuron and optimized with the genetic algorithm. The best relationship of the six was selected for each climate type, and the results were presented in a three-dimensional graph. The best overall relationship obtained in the first method was used as the basic relationship in the second method, and climatic correction coefficients were determined for other climate types using the genetic algorithm optimization model. Finally, the accuracy of the two methods was validated using data from 32 synoptic weather stations throughout Iran. For the first method, error tolerance diagrams and statistical coefficients showed that a quadratic experimental relationship performed best under all climatic conditions. To simplify the method, two graphs were created based on the quadratic relationship for the different climate types, with the axes of the graphs showing relative humidity and temperature, and with pan evaporation, were drawn as contours. For the second method, the quadratic relationship for semi-dry conditions was selected as the basic relationship. The estimated climatic correction coefficients for other climate types lay between 0.8 and 1 for dry, semi-dry, semi-humid, Mediterranean climates, and between 0.4 and 0.6 for humid and very humid climates, indicating that one single relationship cannot be used to simulate pan evaporation for all climatic conditions in Iran. The validation results confirmed the accuracy of the two methods in simulating pan evaporation under different climatic conditions in Iran.

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