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.

scholarly journals Reliability optimization on power systems network using genetic algorithm

2020 ◽  
Vol 2 ◽  
pp. 18-29
Author(s):  
A. E. Airoboman ◽  
Emmanuel A. Ogujor
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Inequality Constraints ◽  
Optimization Approach ◽  
Reliability Optimization ◽  
Equality And Inequality Constraints ◽  
The Mean ◽  
Mean Time ◽  
Reliability And Availability ◽  
Selection Of

In this study, reliability optimization of a non-linear transmission network using Genetic Algorithm (GA) based optimization approach is presented and proposed. A GA based algorithm was developed for Koko, Guinness, Nekpenekpen, Ikpoba-Dam, Switch station, Etete and GRA 33kV tertiary transmission feeders within Benin Metropolis, Nigeria and was used to determine the optimal performance of the feeders’ reliability and availability through the minimization of downtime and the Mean Time between Failure (MTBF) by the appropriate selection of the objective functions and constraints. The equality and inequality constraints for each feeder on the network were defined, thereafter, codes were written on the Matlab 2016a environment to optimize the selected parameters. The results from the study showed a reduction in downtime of 5.63%, 26.87%, 34.20%, 5.42% 8.37%, 5.18% and 10.97% and an increment increased in MTBF by 4.95%, 19.87%, 4.58%, 3.85%, 4.88%, 5.77% and 13.56% for Guinness, Etete, Nekpenekpen, GRA, Switch station and Ikpoba-Dam feeders respectively. The obtained results, therefore, yielded an average corresponding improvement on the network’s reliability and availability by 1.85% and 2.83% respectively. Conclusively, the desired result reached in this paper validates the robustness of the GA tool in reliability studies. However, conscious effort must be geared concerning the ways and manners the system is operated in order to achieve desired results.

Task Scheduling of Parallel Development Projects Using Genetic Algorithms

2004 ◽  
Author(s):  
Miao Zhuang ◽  
Ali A. Yassine
Keyword(s):  
Genetic Algorithm ◽  
Resource Constraints ◽  
Optimal Solution ◽  
Local Optimum ◽  
Development Projects ◽  
Scheduling Problems ◽  
Global Optimal Solution ◽  
Genetic Algorithm Approach ◽  
Project Scheduling Problem ◽  
Global Optimal

Resources for development projects are often scarce in the real world. Generally, many projects are to be completed that rely on a common pool of resources. Besides resource constraints, there exists data dependency among tasks within each project. A genetic algorithm approach with one-point uniform crossover and a refresh operator is proposed to minimize the overall duration or makespan of multiple projects in a resource constrained multi project scheduling problem (RCMPSP) without violating inter-project resource constraints or intra-project precedence constraints. The proposed GA incorporates stochastic feedback or rework of tasks. It has the capability of capturing the local optimum for each generation and therefore ensuring a global best solution. The proposed Genetic Algorithm, with several variants of GA parameters is tested on sample scheduling problems with and without stochastic feedback. This algorithm demonstrates to provide a quick convergence to a global optimal solution and detect the most likely makespan range for parallel projects of tasks with stochastic feedback.

Multi-Objective Optimization for a Novel Electrostatic-Feedback Micro-Sensor Based on Genetic Algorithm

2007 ◽  
Author(s):  
Yongquan Wang ◽  
Hualing Chen ◽  
Zhiying Ou ◽  
Xueming He
Keyword(s):  
Genetic Algorithm ◽  
Multidisciplinary Design Optimization ◽  
Optimal Solution ◽  
Optimization Approach ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Micro Sensor ◽  
Energy Relations

In this paper, we present the multi-objective optimization for an entire microsystem, a novel capacitive electrostatic feedback accelerometer. From the energy relations of the coupled electrostatic-field, the dynamic model of the system is constructed. Aiming at the global performance, a multi-objective optimization model, where sensitivity, resolution and damping resonant frequency are selected as objectives, is established based on the concept of multidisciplinary design optimization (MDO). Genetic algorithm (GA) is used to solve this problem, and compared with a traditional optimization approach, sequence quadratic programming (SQP). Both the two algorithms can achieve our aim commendably, and the optimal solution given by GA is more satisfied. The research provides us a good foundation to develop the stochastic and implicit parallel properties of GA to obtain Pareto optimal solutions.

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.

scholarly journals ECONOMIC DISPATCH - A COMPARATIVE STUDY

2014 ◽  
pp. 186-189
Author(s):  
APOORVA H. ◽  
GARIMA SINHA ◽  
PUNAM DAM ◽  
AMRITA PRADHAN ◽  
RRAJENDER REDDY K.
Keyword(s):  
Genetic Algorithm ◽  
Iterative Method ◽  
Comparative Study ◽  
Economic Dispatch ◽  
Heuristic Method ◽  
Iterative Approach ◽  
Power Demand ◽  
Matlab Software ◽  
Genetic Algorithm Approach ◽  
The Given

In this paper we are comparing the widely used Heuristic Method and Genetic Algorithm with a faster and effective non-iterative “λ-Logic Based” algorithm for ED (Economic Dispatch) of Thermal Units. The non-iterative method uses pre-prepared data to obtain the solution for a specified power demand. Genetic Algorithm performs directed random searches through a given set of alternative with the aim of finding the best alternative with respect to the given criteria of goodness. In this paper ED problem is solved using iterative method by Heuristic method , Non-iterative approach by “ƛlogic Based” technique and genetic Algorithm approach (GA) .The proposed algorithms are tested on 3 and 38 bus system using MATLAB software and the results reported in the paper.

scholarly journals The integrated cutting and packing heterogeneous precast beams multiperiod production planning problem  

2021 ◽  
Author(s):  
Kennedy Anderson Gumarães de Araújo ◽  
Tibérius de Oliveira e Bonates ◽  
Bruno Prata
Keyword(s):  
Genetic Algorithm ◽  
Production Planning ◽  
Programming Model ◽  
Optimal Solution ◽  
Planning Problem ◽  
Cutting And Packing ◽  
Novel Variant ◽  
Production Planning Problem ◽  
Genetic Algorithm Approach ◽  
Feasible Solutions

We introduce a novel variant of cutting production planning problems named Integrated Cutting and Packing Heterogeneous Precast Beams Multiperiod Production Planning (ICP-HPBMPP). We propose an integer linear programming model for the ICP-HPBMPP, as well as a lower bound for its optimal objective function value, which is empirically shown to be closer to the optimal solution value than the bound obtained from the linear relaxation of the model. We also propose a genetic algorithm approach for the ICP-HPBMPP as an alternative solution method. We discuss computational experiments and propose a parameterization for the genetic algorithm using D-optimal experimental design. We observe good performance of the exact approach when solving small-sized instances, although there are difficulties in finding optimal solutions for medium and large-sized problems, or even in finding feasible solutions for large instances. On the other hand, the genetic algorithm is shown to typically find good-quality solutions for large-sized instances within short computing times.

scholarly journals INVESTMENT PORTFOLIO OPTIMIZATION BY APPLYING A GENETIC ALGORITHM-BASED APPROACH

Ekonomika ◽  
2017 ◽  
Vol 96 (2) ◽  
pp. 66-78 ◽  
Author(s):  
Petras Dubinskas ◽  
Laimutė Urbšienė
Keyword(s):  
Genetic Algorithm ◽  
Portfolio Optimization ◽  
Stock Exchange ◽  
Optimization Approach ◽  
Investment Portfolio ◽  
Market Index ◽  
Key Issues ◽  
Genetic Algorithm Approach ◽  
One Year ◽  
Investment Portfolio Optimization

The investment portfolio optimization issues have been widely discussed by scholars for more than 60 years. One of the key issues that emerge for researchers is to clarify which optimization approach helps to build the most efficient portfolio (in this case, the efficiency refers to the minimization of the investment risk and the maximization of the return). The objective of the study is to assess the fitness of a genetic algorithm approach in optimizing the investment portfolio. The paper analyzes the theoretical aspects of applying a genetic algorithm-based approach, then it adapts them to practical research. To build an investment portfolio, four Lithuanian enterprises listed on the OMX Baltics Stock Exchange Official List were selected in accordance with the chosen criteria. Then, by applying a genetic algorithm-based approach and using MatLab software, the optimum investment portfolio was constructed from the selected enterprises. The research results showed that the genetic algorithm-based portfolio in 2013 reached a better risk-return ratio than the portfolio optimized by the deterministic and stochastic programing methods. Also, better outcomes were achieved in comparison with the OMX Baltic Market Index. As a result, the hypothesis of the superiority of a portfolio, optimized on the basis of a genetic algorithm, is not rejected. However, it should be noted that in seeking for more reliable conclusions, further research should include more trial periods as the current study examined a period of one year. In this context, the operation of the approach in the context of a market downturn could be of particular interest.

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