Optimal Riser Design by STL Arbitrarily Slitting Method and Genetic Algorithm

2011 ◽  
Vol 704-705 ◽  
pp. 1349-1355 ◽  
Author(s):  
Xu Shen ◽  
Li Liang Chen ◽  
Jian Xin Zhou ◽  
Xiong Shao ◽  
Dun Ming Liao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Numerical Optimization ◽  
Optimal Size ◽  
Feeding System ◽  
Proper Position ◽  
Slitting Method ◽  
Riser Design ◽  
Cae System ◽  
Casting Optimization

It is essential to consider the proper position and volume of riser during the design of feeding system. In this paper, we are presenting a new method of designing optimal riser in the steel casting processes. A technique of slitting arbitrarily 3D entity model by STL (Stereolithography) was used to obtain accurate values of the partial modulus of casting, and then a mathematical model of the process of the riser design was optimized by a genetic algorithm (GA); with the help of the CAE system, which has an ability to calculate automatically and verify the validity of the optimized results, we will pursue the goal of obtaining the desired riser with optimal size and distribution but without causing any defect in casting. Thus, by combining the numerical optimization with the traditional riser design, our method proposed here will be more practical and reliable. In the end, we give an example to demonstrate the feasibility of our optimized approach in the riser design. Keywords：Riser design, Genetic Algorithm, casting optimization, STL

Research on cargo-loading optimization based on genetic and fuzzy integration

2021 ◽  
Vol 40 (4) ◽  
pp. 8493-8500
Author(s):  
Yanwei Du ◽  
Feng Chen ◽  
Xiaoyi Fan ◽  
Lei Zhang ◽  
Henggang Liang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Three Dimensional ◽  
Distribution Center ◽  
Long Time ◽  
Low Efficiency ◽  
Decision Making Model ◽  
The Mathematical Model ◽  
Loading Scheme

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.

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

scholarly journals Using a Genetic Algorithm to Parameterize a Mathematical Model of a Porcine Ventricular Cardiomyocyte

2021 ◽  
Vol 120 (3) ◽  
pp. 333a
Author(s):  
Taylor K. Pullinger ◽  
Matthew Amoni ◽  
Itziar Irurzun-Arana ◽  
Karin R. Sipido ◽  
Eric A. Sobie
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model

Optimum Selection of Hydraulic Devices for Water Hammer Control in the Pipeline Systems Using Genetic Algorithm

2003 ◽  
Author(s):  
Bong Seong Jung ◽  
Bryan W. Karney
Keyword(s):  
Genetic Algorithm ◽  
Steady State ◽  
Optimization Problems ◽  
System Optimization ◽  
Water Hammer ◽  
Pipeline System ◽  
Optimal Size ◽  
Protection Strategy ◽  
Hydraulic Devices ◽  
Selection Of

Genetic algorithms have been used to solve many water distribution system optimization problems, but have generally been limited to steady state or quasi-steady state optimization. However, transient events within pipe system are inevitable and the effect of water hammer should not be overlooked. The purpose of this paper is to optimize the selection, sizing and placement of hydraulic devices in a pipeline system considering its transient response. A global optimal solution using genetic algorithm suggests optimal size, location and number of hydraulic devices to cope with water hammer. This study shows that the integration of a genetic algorithm code with a transient simulator can improve both the design and the response of a pipe network. This study also shows that the selection of optimum protection strategy is an integrated problem, involving consideration of loading condition, device and system characteristics, and protection strategy. Simpler transient control systems are often found to outperform more complex ones.

A Genetic Algorithm for Scheduling and Decomposition of Multidisciplinary Design Problems

1995 ◽  
Author(s):  
Stephen S. Altus ◽  
Ilan M. Kroo ◽  
Peter J. Gage
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Heuristic Method ◽  
Merit Function ◽  
Multidisciplinary Design ◽  
Design Problems ◽  
Design Studies ◽  
Complex Design ◽  
Complex Engineering

Abstract Complex engineering studies typically involve hundreds of analysis routines and thousands of variables. The sequence of operations used to evaluate a design strongly affects the speed of each analysis cycle. This influence is particularly important when numerical optimization is used, because convergence generally requires many iterations. Moreover, it is common for disciplinary teams to work simultaneously on different aspects of a complex design. This practice requires decomposition of the analysis into subtasks, and the efficiency of the design process critically depends on the quality of the decomposition achieved. This paper describes the development of software to plan multidisciplinary design studies. A genetic algorithm is used, both to arrange analysis subroutines for efficient execution, and to decompose the task into subproblems. The new planning tool is compared with an existing heuristic method. It produces superior results when the same merit function is used, and it can readily address a wider range of planning objectives.

scholarly journals Determining the Rational Motion Intensity of Train Traffic Flows on the Railway Corridors with Account for Balance of Expenses on Traction Resources and Cargo Owners

2019 ◽  
Vol 20 (3) ◽  
pp. 215-228 ◽  
Author(s):  
Tetiana Butko ◽  
Mykhailo Muzykin ◽  
Andrii Prokhorchenko ◽  
Halyna Nesterenko ◽  
Halyna Prokhorchenko
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Reference Station ◽  
Limited Resources ◽  
Rolling Stock ◽  
Traffic Flows ◽  
Railway Transport ◽  
Real Coded Genetic Algorithm ◽  
Cargo Transportation ◽  
Destination Point

Abstract The article proposes a method for determining the rational motion intensity of specific train traffic flows on railway transport corridors with account for balance of expenses on traction resources and cargo owners. A mathematical model based on stochastic optimization is developed, which allows to optimize, in the conditions of risks, the interval between trailing trains on the railway lines taking into account the limited resources of the traction rolling stock, the capacity of the stations and freight fronts at the cargo destination point. Solving this mathematical model allows to find a balance between the expenses for movement of train traffic flows from different railway lines to their terminal reference station and the expenses of a consignee, subject to the limitations of the technological logistics chain in cargo transportation. For the solution of this mathematical model, a Real-coded Genetic Algorithm (RGA) was used.

Implementation of Genetic-Algorithm-Based Forecasting Model to Power System Problems

2018 ◽  
pp. 140-155
Author(s):  
Sajad Madadi ◽  
Morteza Nazari-Heris ◽  
Behnam Mohammadi-Ivatloo ◽  
Sajjad Tohidi
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Power System ◽  
Load Forecasting ◽  
Wind Farms ◽  
Optimization Method ◽  
Forecasting Model ◽  
Renewable Generation ◽  
Forecasting Methods ◽  
Pool Market

Power system includes many types of markets. Such markets are generally cleared at certain times, whereas market participators have to determine their operational plans before meeting the actual conditions. Therefore, forecasting methods can assist market players. Forecasting methods are applied to forecast electricity demand. The unknown conditions in the power system are increased by integration of renewable generation units. Forecasting methods, which are used for the load forecasting, are updated because the output power of renewable generation units such as wind farms and photovoltaic (PV) panels have more deviation than power demand. The pool market can be introduced as other parameter that is forecasted by market players. In this chapter, the authors investigate a mathematical model for forecasting of wind. Then, the forecasting model is proposed. Genetic algorithm is applied as an optimization method to handle delay associated with wind forecasting.

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