Analysis of asynchronous distributed multi-master parallel genetic algorithm optimization on CAN bus

Jamshidi, Vahid; Nekoukar, Vahab; Refan, Mohammad Hossein

doi:10.1007/s12530-020-09337-2

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…The transmission volume and the corresponding communication energy consumption will increase the corresponding data processing energy consumption. At the same time, the change of data fusion rate will affect the energy efficiency of nodes in the process of data fusion processing and transmission, and then affect the life cycle of cluster head nodes [ 22 ]. Park et al think that with the rapid increase of the number of smart devices, the amount of data transmission has also exploded, which also requires the continuous development of mobile wireless communication technology to meet people's growing business needs.…”

Section: Related Workmentioning

confidence: 99%

Optimization and Application of Communication Resource Allocation Algorithm for Urban Rail Transit Planning

Fang

Zhang

2022

Computational Intelligence and Neuroscience

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The construction and operation of China’s rail transit system have entered a high-speed development stage, and the rapid increase of train speed and mileage has brought greater challenges to the safety and reliability of the rail transit system. Network planning evaluation is the key to the early decision-making of urban rail transit project, which directly determines the success or failure of the whole project. How to scientifically and reasonably evaluate the urban rail transit information resource network planning has become a difficult problem for many urban planners to solve. Therefore, this paper studies the optimization of the communication resource allocation algorithm and the comprehensive evaluation of its application for urban rail transit planning. In this paper, based on CVNN structure, the network prototype is an extension of RVNN structure. In the abstract, its processing unit is composed of a pair of real-number processors that can realize certain operations. HNN is a fully connected recurrent neural network based on the idea of the energy function, which is helpful to understand the calculation mode of HNN, and the research shows that HNN can solve many combinatorial optimization problems. In addition, the combination of neural network and genetic algorithm with simulated annealing mechanism can also bring new directions for research. On the basis of experimental analysis, it can be concluded that in general, the error reduction rate of the optimization scheme designed in this paper can reach 58.6% on average. In practical application, the accuracy of the optimal bit error rate is 52.4%.

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Section: Related Workmentioning

confidence: 99%

Optimization and Application of Communication Resource Allocation Algorithm for Urban Rail Transit Planning

Fang

Zhang

2022

Computational Intelligence and Neuroscience

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“…It simulates the natural process of gene recombination and evolution and encodes the parameters of the problem to be solved into binary code or decimal code (or other numerical code), i.e., genes and multiple genes form a chromosome (individual). Paired crossover and mutation operations similar to natural selection are then performed on many chromosomes and iterated (i.e., generational inheritance) until the final optimization result is obtained [24]. e basic framework of the GA is as follows: 9, we describe the basic framework for solving the loop selection problem of multilevel nested loops using GAs, with the following steps:…”

Section: Basicmentioning

confidence: 99%

“…break; (21) end if (22) end for (23) end while (24) Generate Chromosome X q ′ that corresponds to L q ′ (25) end function ALGORITHM 2: Chromosome repair. 8 Mathematical Problems in Engineering (6) Fitness evaluation.…”

Section: Basicmentioning

confidence: 99%

Loop Selection for Multilevel Nested Loops Using a Genetic Algorithm

Nie

Zhou

Huang

et al. 2021

Mathematical Problems in Engineering

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Loop selection for multilevel nested loops is a very difficult problem, for which solutions through the underlying hardware-based loop selection techniques and the traditional software-based static compilation techniques are ineffective. A genetic algorithm- (GA-) based method is proposed in this study to solve this problem. First, the formal specification and mathematical model of the loop selection problem are presented; then, the overall framework for the GA to solve the problem is designed based on the mathematical model; finally, we provide the chromosome representation method and fitness function calculation method, the initial population generation algorithm and chromosome improvement methods, the specific implementation methods of genetic operators (crossover, mutation, and selection), the offspring population generation method, and the GA stopping criterion during the GA operation process. Experimental tests with the SPEC2006 and NPB3.3.1 standard test sets were performed on the Sunway TaihuLight supercomputer. The test results indicated that the proposed method can achieve a speedup improvement that is superior to that by the current mainstream methods, which confirm the effectiveness of the proposed method. Solving the loop selection problem of multilevel nested loops is of great practical significance for exploiting the parallelism of general scientific computing programs and for giving full play to the performance of multicore processors.

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“…Undoubtedly, improving software quality, like improving software productivity, has become a problem that must be always concerned and solved in the whole software development process [5]. The purpose of software testing is to generate test data and find the errors in these test data.…”

Section: Introductionmentioning

confidence: 99%

Optimal Model of Software Testing Path Selection Based on Genetic Algorithm and Its Evolutionary Solution

Zhan

2022

Wireless Communications and Mobile Computing

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In software testing, the selection of test data is a difficult problem in structural testing. Whether the test data is appropriate or not is directly related to whether the error can be expected to be detected. In the process of software testing, the generation of test data is not only the core problem but also the key and difficulty of software testing. Because of the huge number of test cases and low test efficiency, a powerful optimization algorithm is needed to optimize the initial test cases. As a robust search method, genetic algorithm shows unique advantages and high efficiency in solving high-complexity problems such as large space, multipeak, nonlinear, and global optimization. Based on the application of genetic algorithm, this paper analyzes the optimization path by classifying and calculating the objective function and introducing NSGA-II algorithm, measures the distance between each branch on the processing path sample set, and sorts the path set to obtain the optimal solution. On the basis of the designed model, the experimental results show that the error control rate of the model is 89.4%. Moreover, because of the superiority of NSGA-II algorithm, the probability of comprehensive cross mutation is increased by 56.7%.

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Analysis of asynchronous distributed multi-master parallel genetic algorithm optimization on CAN bus

Cited by 7 publications

References 11 publications

Optimization and Application of Communication Resource Allocation Algorithm for Urban Rail Transit Planning

Optimization and Application of Communication Resource Allocation Algorithm for Urban Rail Transit Planning

Loop Selection for Multilevel Nested Loops Using a Genetic Algorithm

Optimal Model of Software Testing Path Selection Based on Genetic Algorithm and Its Evolutionary Solution

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