scholarly journals Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1251
Author(s):  
Sabyasachi Mondal ◽  
Antonios Tsourdos
Keyword(s):  
Genetic Algorithm ◽  
Task Allocation ◽  
Minimum Cost ◽  
Allocation Problem ◽  
Two Dimensional ◽  
Tabular Form ◽  
Comparison Study ◽  
New Variety ◽  
Quantum Genetic Algorithm ◽  
Better Than

This paper presents a Two-Dimensional Quantum Genetic Algorithm (2D-QGA), which is a new variety of QGA. This variety will allow the user to take the advantages of quantum computation while solving the problems which are suitable for two-dimensional (2D) representation or can be represented in tabular form. The performance of 2D-QGA is compared to two-dimensional GA (2D-GA), which is used to solve two-dimensional problems as well. The comparison study is performed by applying both the algorithm to the task allocation problem. The performance of 2D-QGA is better than 2D-GA while comparing execution time, convergence iteration, minimum cost generated, and population size.

scholarly journals A Building-Block-Based Genetic Algorithm for Solving the Robots Allocation Problem in a Robotic Mobile Fulfilment System

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Jingtian Zhang ◽  
Fuxing Yang ◽  
Xun Weng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Scheduling ◽  
Building Blocks ◽  
Allocation Problem ◽  
Accurate Analysis ◽  
Allocation Problems ◽  
Scheduling Method ◽  
Project Scheduling Problem ◽  
Block Based ◽  
Better Than

Robotic mobile fulfilment system (RMFS) is an efficient and flexible order picking system where robots ship the movable shelves with items to the picking stations. This innovative parts-to-picker system, known as Kiva system, is especially suited for e-commerce fulfilment centres and has been widely used in practice. However, there are lots of resource allocation problems in RMFS. The robots allocation problem of deciding which robot will be allocated to a delivery task has a significant impact on the productivity of the whole system. We model this problem as a resource-constrained project scheduling problem with transfer times (RCPSPTT) based on the accurate analysis of driving and delivering behaviour of robots. A dedicated serial schedule generation scheme and a genetic algorithm using building-blocks-based crossover (BBX) operator are proposed to solve this problem. The designed algorithm can be combined into a dynamic scheduling structure or used as the basis of calculation for other allocation problems. Experiment instances are generated based on the characteristics of RMFS, and the computation results show that the proposed algorithm outperforms the traditional rule-based scheduling method. The BBX operator is rapid and efficient which performs better than several classic and competitive crossover operators.

Image Segmentation Research Based on GA and Improved Otsu Algorithm

2017 ◽  
Vol 7 (2) ◽  
pp. 533
Author(s):  
Xiaoqun Qin
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Two Dimensional ◽  
One Dimensional ◽  
Otsu Method ◽  
Segmented Object ◽  
The Face ◽  
Image Segmentation Algorithm ◽  
Small Dispersion ◽  
Better Than

<p>In the face of the problem of high complexity of two-dimensional Otsu adaptive threshold algorithm, a new fast and effective Otsu image segmentation algorithm is proposed based on genetic algorithm. This algorithm replaces the segmentation threshold of the traditional two - dimensional Otsu method by finding the threshold of two one-dimensional Otsu method, it reduces the computational complexity of the partition from O (L4) to O (L). In order to ensure the integrity of the segmented object, the algorithm introduces the concept of small dispersion in class, and the automatic optimization of parameters are achieved by genetic algorithm. Theoretical analysis and experimental results show that the algorithm is not only better than the original two-dimensional Otsu algorithm, but also it has better segmentation effect.</p>

scholarly journals The Evaluation of Dynamic Airport Competitiveness Based on IDCQGA-BP Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Qiang Cui ◽  
Hai-bo Kuang ◽  
Ye Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
River Delta ◽  
Bp Algorithm ◽  
Quantum Genetic Algorithm ◽  
Competition And Cooperation ◽  
Better Than

Aimed at the multidimensional and complex characteristic of airport competitiveness, a new algorithm is proposed in which BP neural network is optimized by improved double chains quantum genetic algorithm (IDCQGA-BP). The new algorithm is better than existing algorithms in convergence and the diversity of quantum chromosomes. The empirical data of eight airports in Yangtze River Delta in 2011 and 2012 is applied to verify the feasibility of the new algorithm, and then the competitiveness of the eight airports from 2013 to 2015 is gotten through the algorithm. The results show the following. (1) The new algorithm is better than the existing optimization algorithms in the aspects of error accuracy and run time. (2) The gaps of the airports in Yangtze River Delta are narrowing; the competition and cooperation are getting stronger and stronger. (3) The main increase reason of airport competitiveness is the increase of own investment.

A New Fuzzing Technique Using Niche Genetic Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 4050-4058 ◽  
Author(s):  
Xue Yong Zhu ◽  
Zhi Yong Wu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Minimum Cost ◽  
Test Cases ◽  
Better Than

Current advanced Fuzzing technique can only implement vulnerability mining on a single vulnerable statement each time, and this paper proposes a new multi-dimension Fuzzing technique, which uses niche genetic algorithm to generate test cases and can concurrently approach double vulnerable targets with the minimum cost on the two vulnerable statements each time. For that purpose, a corresponding mathematical model and the minimum cost theorem are presented. The results of the experiment show that the efficiency of the new proposed Fuzzing technique is much better than current advanced Fuzzing techniques.

scholarly journals Bridge Critical State Search by Using Quantum Genetic Firefly Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Shi-bo Tao ◽  
Dian-zhong Liu ◽  
Ai-ping Tang
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Parameter Optimization ◽  
Optimization Model ◽  
Critical State ◽  
Firefly Algorithm ◽  
Optimal Solution ◽  
Quantum Genetic Algorithm ◽  
Flutter Analysis ◽  
Better Than

When performing flutter analysis through the traditional methods, it is difficult to solve high-order strong nonlinear equations. For overcoming this difficulty, this paper establishes a double-parameter optimization model for searching the flutter critical wind speed and frequency. A new hybrid firefly algorithm called the quantum genetic firefly algorithm is presented to search the optimal solution to the optimization model. The proposed algorithm is the combination of the firefly algorithm and the quantum genetic algorithm. The results of the quantum genetic firefly algorithm are compared with the results shown by the firefly algorithm and quantum genetic algorithm. Numerical and experimental results of the proposed algorithm are competitive and in most cases are better than that of the firefly algorithm and quantum genetic algorithm.

Reconstruction of Two-Dimensional Projections of GP 32*I

1981 ◽  
Vol 39 ◽  
pp. 38-39
Author(s):  
H.A. Cohen ◽  
W. Chiu ◽  
J. Hosoda
Keyword(s):  
Electron Microscopy ◽  
Molecular Weight ◽  
Uranyl Acetate ◽  
Distilled Water ◽  
Acetate Solution ◽  
Two Dimensional ◽  
Parent Molecule ◽  
T4 Bacteriophage ◽  
Electron Imaging ◽  
Better Than

GP 32 (molecular weight 35000) is a T4 bacteriophage protein that destabilizes the DNA helix. The fragment GP32*I (77% of the total weight), which destabilizes helices better than does the parent molecule, crystallizes as platelets thin enough for electron diffraction and electron imaging. In this paper we discuss the structure of this protein as revealed in images reconstructed from stained and unstained crystals.Crystals were prepared as previously described. Crystals for electron microscopy were pelleted from the buffer suspension, washed in distilled water, and resuspended in 1% glucose. Two lambda droplets were placed on grids over freshly evaporated carbon, allowed to sit for five minutes, and then were drained. Stained crystals were prepared the same way, except that prior to draining the droplet, two lambda of aqueous 1% uranyl acetate solution were applied for 20 seconds. Micrographs were produced using less than 2 e/Å2 for unstained crystals or less than 8 e/Å2 for stained crystals.

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