A multi-population state optimization algorithm for rail crack fault diagnosis

2021 ◽  
Author(s):  
Mengmeng Liu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fault Diagnosis ◽  
Optimization Algorithm ◽  
High Stability ◽  
Detection Method ◽  
Optimal Solutions ◽  
Complex Environments ◽  
Fast Convergence Rate ◽  
Low Efficiency

Abstract The rails usually work in complex environments, which makes them more prone to mechanical failures. In order to better diagnose the crack faults, a multi-population state optimization algorithm (MPVHGA) is proposed in this paper, which is used to solve the problems of low efficiency, easy precocity, and easy convergence of local optimal solutions in traditional genetic algorithms. The detection results of fault signals show that MPVHGA has the advantages of fast convergence rate, high stability, no stagnation, and no limitation of fixed iterations number. The average iterations number of MPVHGA in 100 independent iterations is about 1/5 of the traditional genetic algorithm (SGA for short) and about 1/3 of the population state optimization algorithm (VHGA for short), and the total convergence number of MPVHGA converges to 55 and 10 more than SGA and VHGA respectively, and the accuracy of fault diagnosis can reach 95.04%. On the basis of improving the performance of simple genetic algorithm, this paper provides a new detection method for rail crack fault diagnosis, which has important engineering practical value.

Optimal design of an electrical connector intermittent fault–accelerated reproduction test based on a genetic algorithm

2019 ◽  
Vol 233 (5) ◽  
pp. 857-867
Author(s):  
Zunqing Zhu ◽  
Qian Li ◽  
Yong Zhang ◽  
Guanjun Liu ◽  
Jing Qiu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Optimal Design ◽  
Intermittent Faults ◽  
Damage Models ◽  
Intermittent Fault ◽  
Electrical Connector ◽  
Reproduction Test ◽  
Low Efficiency ◽  
Reproduction Efficiency

The intermittent fault of an electrical connector is a latent threat to the reliability of an electromechanical system. For electrical connector intermittent fault diagnosis, an intermittent fault must be reproduced. Reproducing an intermittent fault by a traditional test has a low efficiency and adds some damage to the product, which is not conducive to intermittent fault diagnosis. To further improve the reproduction efficiency of an intermittent fault and reduce the damage, optimal design of a step-stress-accelerated intermittent fault reproduction test is carried out. First, the number of intermittent faults and the degree of damage in the reproduction test are estimated, and reproduction and damage models of an intermittent fault during the step-stress reproduction test are constructed. Then, based on the intermittent fault and damage models, an optimized method based on a genetic algorithm is established. Finally, the validity and applicability of the theoretical model and the optimized method of the step-stress-accelerated test based on a genetic algorithm are verified by comparing data from a contrast test.

scholarly journals Penentuan Jarak Terpendek Pada Jalur Pengiriman Musae Chips Dengan Menggunakan Algoritma Genetika

2019 ◽  
Vol 6 (1) ◽  
pp. 19-23
Author(s):  
Muhammad Bahrul Arif
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Distribution Channel ◽  
Optimal Solutions ◽  
Distribution Lines ◽  
Good Path ◽  
Crossover Probability ◽  
Study Results ◽  
Genetic Algorithm Method

Combination of good path distribution by land can optimize travel time and costs. However, not all of these path distribution combinations will provide the best solution. The study was conducted to determine the distribution of goods so that the best solution is achieved. To simplify the process of determining the goods distribution channel, it is supported by software development. Genetic algorithms that have reliability in producing optimal solutions can be used to solve this problem. The application of the genetic algorithm method is applied in software. In the software that is built, there are several inputs needed, namely: cities destination distribution as the initial chromosome number, number of generations, crossover probability and probability of mutation. The result of processing is a combination of goods distribution lines to be taken which represent the solution to this problem. Only the best chromosomes will be given as a result. Through the software that was built, the determination of goods distribution lines is expected to be better and can optimize the time and cost of travel. Based on the research, 5 road combinations are used as chip distribution routes used for testing. This study results from the first fitness gene get the highest fitness 7.4, fitness lowest 5.6 and the second gene get the highest fitness 9.3, fitness lowest 5.6.

Design of the MEMS-Based Straight-Beam Micromirror Using a Genetic Algorithm

2014 ◽  
Vol 529 ◽  
pp. 193-196
Author(s):  
Qing Hua Chen ◽  
Yan Mei Li ◽  
Ying Jun Chen ◽  
Wen Gang Wu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Optical Fiber ◽  
Optimization Algorithm ◽  
Complex Nature ◽  
Straight Beam ◽  
Good Agreement

A system for optimizing the design of a MEMS mirror that meets a set of specified constraints has been developed to optimally serve optical fiber-based networking applications. An optimization algorithm based on genetic algorithms was implemented for the complex nature of the work. FEM verifications reveal that the optimizing results show a good agreement with the simulated result.

Comparative Analysis of Determinant Elimination Method, Ant Colony Algorithms and Genetic Algorithm

2014 ◽  
Vol 624 ◽  
pp. 509-511
Author(s):  
Xin Fu
Keyword(s):  
Genetic Algorithm ◽  
Comparative Analysis ◽  
Optimization Algorithm ◽  
Ant Colony ◽  
Optimal Solutions ◽  
Optimal Test ◽  
Test Set ◽  
Elimination Method ◽  
Ant Colony Algorithms ◽  
Test Sets

In this paper, the analysis of optimizing test sets of which the optimal solutions are already known is made first. Then the optimization results and execution time of Determinant Elimination Method, Ant Colony Algorithms as well as Genetic Algorithm are compared. At last, Based on the concept of optimal test set proposed in this paper, plenty of test sets which need to be optimized are randomly generated. The optimization algorithm proposed in this paper is also used to optimize the test sets and the consequences of optimization is desirable.

scholarly journals A Genetic Algorithm Based Approach for Solving the Minimum Dominating Set of Queens Problem

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Saad Alharbi ◽  
Ibrahim Venkat
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Dominating Set ◽  
Classical Problem ◽  
Optimal Solutions ◽  
Effective Solution ◽  
Minimum Dominating Set ◽  
Bioinspired Algorithms

In the field of computing, combinatorics, and related areas, researchers have formulated several techniques for the Minimum Dominating Set of Queens Problem (MDSQP) pertaining to the typical chessboard based puzzles. However, literature shows that limited research has been carried out to solve the MDSQP using bioinspired algorithms. To fill this gap, this paper proposes a simple and effective solution based on genetic algorithms to solve this classical problem. We report results which demonstrate that near optimal solutions have been determined by the GA for different board sizes ranging from 8 × 8 to 11 × 11.

scholarly journals Analyzing the Performance of the Multiple-Searching Genetic Algorithm to Generate Test Cases

2020 ◽  
Vol 10 (20) ◽  
pp. 7264
Author(s):  
Wanida Khamprapai ◽  
Cheng-Fa Tsai ◽  
Paohsi Wang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Execution Time ◽  
Multicast Routing ◽  
Test Case ◽  
Test Cases ◽  
Mutation Operator ◽  
Optimal Solutions ◽  
Crossover Operator ◽  
Network Systems

Software testing using traditional genetic algorithms (GAs) minimizes the required number of test cases and reduces the execution time. Currently, GAs are adapted to enhance performance when finding optimal solutions. The multiple-searching genetic algorithm (MSGA) has improved upon current GAs and is used to find the optimal multicast routing in network systems. This paper presents an analysis of the optimization of test case generations using the MSGA by defining suitable values of MSGA parameters, including population size, crossover operator, and mutation operator. Moreover, in this study, we compare the performance of the MSGA with a traditional GA and hybrid GA (HGA). The experimental results demonstrate that MSGA reaches the maximum executed branch statements in the lowest execution time and the smallest number of test cases compared to the GA and HGA.

scholarly journals Creating of model of genetic algorithm used to optimization of construction of jet engine element

Mechanik ◽  
2017 ◽  
Vol 90 (7) ◽  
pp. 603-605
Author(s):  
Adam Kozakiewicz ◽  
Rafał Kieszek
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Evolutionary Algorithm ◽  
Optimization Algorithm ◽  
Turbine Engines ◽  
Jet Engine ◽  
Complete Problem ◽  
Traditional Technology ◽  
Np Complete

In this paper authors show results of optimization of compressor discs in turbine engines. The problem of optimizing the thickness of the disc brought to the NP-complete problem, and solved it by using one of the genetic algorithms – evolutionary algorithm. Correctness of model and optimization algorithm were constantly checked. At the end of this paper, compressor disc created due to traditional technology and disc created by BLISK technology were compared.

scholarly journals Adjust genetic algorithm parameter by fuzzy system

2015 ◽  
Vol 37 ◽  
pp. 190
Author(s):  
Tayebe Noshadi ◽  
Marzieh Dadvar ◽  
Nastaran Mirza ◽  
Shima Shamseddini
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Genetic Evolution ◽  
Optimal Solutions ◽  
Heuristic Rules ◽  
Parallel Genetic Algorithms ◽  
Genetic Algorithm Parameter

Genetic algorithm is one of the random searches algorithm. Genetic algorithm is a method that uses genetic evolution as a model of problem solving. Genetic algorithm for selecting the best population, but the choices are not as heuristic information to be used in specific issues. In order to obtain optimal solutions and efficient use of fuzzy systems with heuristic rules that we would aim to increase the efficiency of parallel genetic algorithms using fuzzy logic immigration, which in fact do this by optimizing the parameters compared with the use of fuzzy system is done.

Workshop Layout Design Based on Genetic Algorithm

2013 ◽  
Vol 739 ◽  
pp. 721-724
Author(s):  
Song Yan Zhang
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Research Method ◽  
Biological Evolution ◽  
Layout Design ◽  
Evolution Process ◽  
Optimal Solutions

Genetic algorithm is a kind of global stochastic research method that simulates biological evolution process to achieve optimal results. In this paper, we use genetic algorithm in the workshop layout design. The result shows that genetic algorithms can not only get excellent near optimal solutions, but also have high computing efficiency and practicability.

scholarly journals SISTEM PENJADWALAN MATA PELAJARAN MENGGUNAKAN ALGORITMA GENETIKA

2019 ◽  
Vol 3 (1) ◽  
pp. 49
Author(s):  
Yesri Elva
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Problem Solving ◽  
Optimization Algorithm ◽  
Learning Process ◽  
Optimal Solution ◽  
Complex Problem ◽  
Complex Problem Solving ◽  
Algorithm Optimization ◽  
Fitness Value

Abstract - Schedule is one important factor to support the learning process, one of which at SMKN 3 Pariaman. In SMKN 3 Pariaman scheduling process is still done manually, consequently there are conflicting schedules and timing of learning becomes too late. One of completion method to the problem is to use a genetic algorithm, because it is one of the Genetic Algorithm optimization algorithm that is robust and can be used on a wide variety of case studies such as scheduling. This algorithm is also often used to find the optimal solution both in the case of simple to complex problem-solving technique that determines the start and initialization pupulasi chromosomes, determine the value of fitness, selection, crossover, mutation. Mutations done to produce the best fitness value which can be used to determine the final outcome scheduling. If the best fitness values have been obtained, the process is stopped and reach the finish condition.Keywords - Genetic Algorithms, Scheduling Abstrak - Jadwal merupakan salah satu faktor penting untuk penunjang proses belajar mengajar, salah satunya pada SMKN 3 Pariaman. Pada SMKN 3 Pariaman proses penyusunan jadwal masih dilakukan secara manual, akibatnya masih terdapat jadwal yang bentrok dan waktu pelaksanaan belajar mengajar menjadi terlambat. Salah satu metode untuk penyelesain masalah tersebut adalah dengan menggunakan algoritma genetika, karena Algoritma Genetika merupakan salah satu algoritma optimasi yang kuat dan bisa digunakan pada berbagai macam studi kasus seperti penjadwalan. Algoritma ini juga sering digunakan untuk mencari solusi optimal baik pada kasus yang sederhana sampai yang rumit teknik pemecahan masalahnya yaitu menentukan pupulasi awal dan inisialisasi kromosom, menentukan nilai fitness, seleksi crossover, mutasi. Mutasi dilakukan sampai menghasilkan nilai fitness terbaik yang dapat digunakan untuk penentuan hasil akhir penyusunan jadwal. Jika nilai fitness terbaik sudah didapatkan maka proses dihentikan dan mencapai kondisi selesai.Kata kunci  - Algoritma Genetika, Penjadwalan

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