A Reliability Optimization Allocation Method Considering Differentiation of Functions

2016 ◽  
Vol 13 (04) ◽  
pp. 1641020 ◽  
Author(s):  
X. J. Yi ◽  
Y. H. Lai ◽  
H. P. Dong ◽  
P. Hou
Keyword(s):  
Genetic Algorithm ◽  
Optimal Solution ◽  
Reliability Function ◽  
New Method ◽  
Reliability Optimization ◽  
Main Function ◽  
Actual Situation ◽  
Allocation Method ◽  
Importance Factor ◽  
Optimization Allocation

The reliability optimization has achieved great concern in recent years. Nowadays, many researchers obtain allocation results which can maximize the system reliability subject to the system budget. In these researches, the effect of system’s functions is always neglected or only considering the single main function of system. In addition, there are also no obvious evidences in results to distinguish the importance level of different units. However, complex systems tend to perform multiple functions. What’s more, the use frequency of each function and the combinations of units to realize different functions are not the same. In addition, the use demand of different functions is decided by different task environments, the demand differentiation of functions has led to the usage of frequency of various functions having different levels about reliability. Therefore, the reliability optimization allocation only considering cost constraint conditions is not accurate and will results in disaccord between the obtained results with actual situation. Focusing on the problem mentioned above, a reliability optimization allocation method that considers cost constraint and importance factor is proposed. In this paper, we consider systems consisting of units characterized by different reliability and importance factors. Such systems are multi-function because they must perform different tasks depending on the combination of units. Different functions may work simultaneously. Firstly, the concept of importance factor is defined to describe the importance of a unit and the required importance factor level of system functions in the task is also given. To deal with the differentiation of system functions, the corresponding bound about importance factor are executed when looking for the optimal solution. Similarly, the cost constraint is also forced. Finally, in order to reduce the randomness of intelligent algorithm, a number of optimization are conducted and a rule is proposed to select the most optimal solution from all the optimal solutions which are obtained in every iterative loop. Example of an integrated transmission device is presented. To begin with, we establish the reliability function of system as the objective optimization function. Then, the restraint of budget and different demands of importance factor of system functions are posed. Furthermore, using a genetic algorithm as the optimization tool, the optimization result can be obtained. Finally, the most optimal solution is selected. The results show that, the method, we proposed is more correct and more approximate than the reality. To verify the advantages and engineering applicability of the new method, the results obtained by the new method are compared with the results obtained under different conditions using basic genetic algorithm, without considering functions and the differentiation of functions, to solve the allocation problem of integrated transmission device, respectively. The reliability optimization allocation method presented in this paper can not only consider the constraint of cost but also can consider the diversities of functions, and thus the optimization results will be more approximate in actual situation. At the same time, this paper can also provide guidance for the similar reliability optimization problem.

A Reliability Optimization Allocation Method for Multifunction System With Differentiation of Functions

2015 ◽  
Author(s):  
Xiaojian Yi ◽  
Peng Hou ◽  
Haiping Dong ◽  
Yuehua Lai ◽  
Huina Mu
Keyword(s):  
Genetic Algorithm ◽  
Generating Function ◽  
System Reliability ◽  
Reliability Optimization ◽  
Main Function ◽  
System Function ◽  
Improved Genetic Algorithm ◽  
Reliability Allocation ◽  
Power Shift ◽  
Optimization Allocation

The study on the optimization of system reliability allocation rarely involved the constraints on different functions of system. Some constraints only referred to the main function or one function in system. Owing to the requirement of mission and other factors, all system functions need to have different performance and reliability respectively. For this reason, we proposed a new method for optimization of reliability allocation in this paper. The constrains in the method focus on the discrepancy of reliability of different functions of system. The reliability of system function is defined as it whether to meet the requirement of mission capacity, and the reliability of all different system functions will be calculated by universal generating function, and the objective is to minimize system cost. At last, this method is applied in reliability optimization allocation of a Power-shift Steering Transmission with the improved genetic algorithm.

An Improved Cloud Adaptive Genetic Algorithm Based on Cloud Computing for Active Optimization Calculation

2015 ◽  
Vol 713-715 ◽  
pp. 1579-1582
Author(s):  
Shao Min Zhang ◽  
Ze Wu ◽  
Bao Yi Wang
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Large Scale ◽  
Optimal Solution ◽  
Adaptive Genetic Algorithm ◽  
Load Optimization ◽  
Active Load ◽  
Optimization Calculation ◽  
Local Optimal Solution ◽  
Optimization Allocation

Under the background of huge amounts of data in large-scale power grid, the active power optimization calculation is easy to fall into local optimal solution, and meanwhile the calculation demands a higher processing speed. Aiming at these questions, the farmer fishing algorithm which is applied to solve the problem of optimal distribution of active load for coal-fired power units is used to improve the cloud adaptive genetic algorithm (CAGA) for speeding up the convergence phase of CAGA. The concept of cloud computing algorithm is introduced, and parallel design has been done through MapReduce graphs. This method speeds up the calculation and improves the effectiveness of the active load optimization allocation calculation.

Reliability Optimization Allocation Method for Multifunction Systems With Multistate Units Based on Goal-Oriented Methodology

2017 ◽  
Vol 3 (4) ◽  
Author(s):  
Xiao-jian Yi ◽  
B. S. Dhillon ◽  
Jian Shi ◽  
Hai-na Mu ◽  
Peng Hou
Keyword(s):  
Reliability Optimization ◽  
Allocation Model ◽  
New Approach ◽  
Reliability Indices ◽  
Analysis Process ◽  
Allocation Method ◽  
Power Shift ◽  
Go Model ◽  
Go Methodology ◽  
Optimization Allocation

This paper proposes a new reliability optimization allocation for multifunction systems with multistate units based on goal-oriented (GO) methodology. First, this optimization allocation method is expounded in terms of establishing GO model, establishing reliability optimization allocation model, and solving algorithm. Then its process is formulated. Finally, the new method is applied in reliability optimization allocation of power-shift steering transmission (PSST), whose goal is to minimize the system cost. The results analysis shows that the system costs for different operation times turn to a relatively stable value, and the allocated reliability indices of unit are satisfied with engineering requirements. All in all, this new optimization allocation method can not only obtain the reasonable allocation results quickly and effectively, but it also can overcome the disadvantages of existing reliability optimization allocation methods for complex multifunction systems efficiently. In addition, the analysis process shows that the reliability optimization allocation method based on GO method can provide a new approach for the reliability optimization allocation of multifunction systems with multistate units.

A New Testability Optimization Allocation Approach

2013 ◽  
Vol 328 ◽  
pp. 444-449 ◽  
Author(s):  
Gang Liu ◽  
Fang Li
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Iterative Process ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Numerical Example ◽  
Set Up ◽  
Optimization Allocation ◽  
Allocation Approach

This paper describes a methodology based on improved genetic algorithms (GA) and experiments plan to optimize the testability allocation. Test resources were reasonably configured for testability optimization allocation, in order to meet the testability allocation requirements and resource constraints. The optimal solution was not easy to solve of general genetic algorithm, and the initial parameter value was not easy to set up and other defects. So in order to more efficiently test and optimize the allocation, migration technology was introduced in the traditional genetic algorithm to optimize the iterative process, and initial parameters of algorithm could be adjusted by using AHP approach, consequently testability optimization allocation approach based on improved genetic algorithm was proposed. A numerical example is used to assess the method. and the examples show that this approach can quickly and efficiently to seek the optimal solution of testability optimization allocation problem.

Reliability Optimization Allocation Method for Multifunction Systems Based on Goal Oriented Methodology

2016 ◽  
Author(s):  
Xiaojian Yi ◽  
B. S. Dhillon ◽  
Jian Shi ◽  
Hui-na Mu ◽  
Peng Hou
Keyword(s):  
System Reliability ◽  
Reliability Index ◽  
Mathematic Model ◽  
Allocation Problem ◽  
Reliability Optimization ◽  
Constraint Function ◽  
System Cost ◽  
Allocation Method ◽  
Reliability Constraint ◽  
Optimization Allocation

This paper proposes a new reliability optimization allocation for multifunction systems based on GO methodology. First, two constraints functions are proposed, which are unit reliability constraint function and system reliability constraint function, respectively. The unit reliability constraint function consists of allocated reliability index of unit and the range of reliability index for unit. And the system reliability constraint function consists of the target reliability index of system, and the predicted reliability index of system obtained by using GO method and allocated reliability index of unit. Then, the objective function of optimization allocation problem is established to describe the system cost minimization taking into consideration costs of unit redesigned and unit selected versions. Based on above, the mathematic model of reliability optimization allocation problem for complex multifunction systems is established. In addition, an improved genetic algorithm is presented to solve this mathematic model. Furthermore, the process of the new reliability optimization allocation method for complex multifunction systems is formulated. Finally, the new method is applied in reliability optimization allocation of Power-shift Steering Transmission whose goal is to minimize the system cost. The results analysis shows that the system costs for different operation times turn to a relatively stable value, and the allocated reliability indexes of unit are satisfied with engineering requirements. All in all, this new optimization allocation method can not only obtain the reasonable allocation results quickly and effectively, but it also can overcome the disadvantages of existing reliability optimization allocation methods for complex multifunction systems efficiently. In addition, the analysis process shows that the reliability optimization allocation method based on GO method can provide a new approach for the reliability optimization allocation of multifunction systems.

scholarly journals Machine Reliability Optimization by Genetic Algorithm Approach

2020 ◽  
pp. 35-40
Author(s):  
Ngnassi Djami Aslain Brisco ◽  
Nzie Wolfgang ◽  
Doka Yamigno Serge
Keyword(s):  
Genetic Algorithm ◽  
Heuristic Method ◽  
Reliability Assessment ◽  
Optimal Solution ◽  
Assessment Method ◽  
Optimization Approach ◽  
Reliability Optimization ◽  
Machine Reliability ◽  
Long Time ◽  
Genetic Algorithm Approach

To define the reliability network of a system (machine), we start with a set of components arranged in an appropriate topology (series, parallel, or parallel-series), choose the best terms of the ratio performance / cost, and gather by links with the aim to combine them. This process requires a long time and effort, given the very large number of possible combinations, which becomes tedious for the analyst. For this reason, it is essential to use an appropriate optimization approach when designing any product. However, before trying to optimize, it is necessary to have a reliability assessment method. The objective of this paper is to display a meta-heuristic method, which is sustained on the genetic algorithm (GA) to improve the machines reliability. To achieve this objective, a methodology that consists of presenting the functionalities of genetic algorithms is developed. The result achieved is the proposal of a reliability network for the optimal solution.

A New Reliability Optimization Allocation Method for Nuclear Power Systems Based on Goal Oriented Methodology

2017 ◽  
Author(s):  
Yi Xiao-jian ◽  
Xie Yong-cheng ◽  
Shi Jian ◽  
Mu Hui-na ◽  
Hou Peng
Keyword(s):  
Power Systems ◽  
System Reliability ◽  
Nuclear Power ◽  
System Analysis ◽  
Mathematic Model ◽  
Reliability Optimization ◽  
New Approach ◽  
Allocation Method ◽  
Go Model ◽  
Optimization Allocation

This paper presents a new reliability optimization allocation method for complex nuclear power systems based on Goal Oriented (GO) method, whose goal is to minimize the system cost. First, the new reliability optimization allocation method is expounded in detail. And its process is formulated. Then, the hoisting mechanism in nuclear power plant is taken as an example to allocate its system reliability index to design unit by the new method. The reliability optimization allocation processes are mainly as follows: (i) Conducting system analysis, (ii) Developing GO model, (iii) Establishing reliability optimization allocation mathematic model, (iv) Solving the reliability optimization allocation mathematic model, (v) Determining the allocation results. The results analysis shows that the system costs for different solving times turn to a relatively stable value, and the allocated reliability indexes of unit are satisfied with engineering requirements. All in all, this new reliability optimization allocation method can not only obtain the reasonable allocation results quickly and effectively, but it also can overcome the disadvantages of existing reliability optimization allocation methods for complex nuclear power systems efficiently. Moreover, this paper provides a new approach for the reliability optimization allocation of complex nuclear power systems.

Dynamic Sub-Population Genetic Algorithm Combined with Dynamic Penalty Function to Solve Constrained Optimization Problems

2010 ◽  
Vol 450 ◽  
pp. 560-563
Author(s):  
Dong Mei Cheng ◽  
Jian Huang ◽  
Hong Jiang Li ◽  
Jing Sun
Keyword(s):  
Genetic Algorithm ◽  
Constrained Optimization ◽  
Penalty Function ◽  
Local Convergence ◽  
Population Genetic ◽  
Optimization Problems ◽  
Optimal Solution ◽  
New Method ◽  
Constrained Optimization Problems ◽  
Population Genetic Algorithm

This paper presents a new method of dynamic sub-population genetic algorithm combined with modified dynamic penalty function to solve constrained optimization problems. The new method ensures the final optimal solution yields all constraints through re-organizing all individuals of each generation into two sub-populations according to the feasibility of individuals. And the modified dynamic penalty function gradually increases the punishment to bad individuals with the development of the evolution. With the help of the penalty function and other improvements, the new algorithm prevents local convergence and iteration wandering fluctuations. Typical instances are used to evaluate the optimizing performance of this new method; and the result shows that it can deal with constrained optimization problems well.

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