Research on Network Congestion Control Based on Quantum Genetic Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 845-849
Author(s):  
Gang Lei ◽  
Xia Yin ◽  
Wei Shi
Keyword(s):  
Genetic Algorithm ◽  
Congestion Control ◽  
Network Performance ◽  
High Efficiency ◽  
Optimal Solution ◽  
Network Congestion ◽  
Gate Operation ◽  
Quantum Bit ◽  
Quantum Genetic Algorithm ◽  
Network Congestion Control

An optimization mathematical model of QoS routing, which with the objectives of network congestion control, is presented in this paper. Combining quantum computation and genetic algorithm, an algorithm for network congestion control based on quantum genetic algorithm (QGA) is proposed. Quantum bit and quantum rotation gate operation are used to update the chromosomes. The simulation results manifest that this algorithm is of fast speed and high efficiency and easily escape from local optimal solution. It can improve network performance and arrive at the purpose of congestion control.

scholarly journals Optimization of Network Coding Resources Based on Improved Quantum Genetic Algorithm

Photonics ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 502
Author(s):  
Tianyang Liu ◽  
Qiang Sun ◽  
Huachun Zhou ◽  
Qi Wei
Keyword(s):  
Genetic Algorithm ◽  
Network Coding ◽  
Topological Structure ◽  
Rotation Angle ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Resource Optimization ◽  
Quantum Genetic Algorithm ◽  
Adjustment Mechanism ◽  
Adaptive Adjustment

The problem of network coding resource optimization with a known topological structure is NP-hard. Traditional quantum genetic algorithms have the disadvantages of slow convergence and difficulty in finding the optimal solution when dealing with this problem. To overcome these disadvantages, this paper proposes an adaptive quantum genetic algorithm based on the cooperative mutation of gene number and fitness (GNF-QGA). This GNF-QGA adopts the rotation angle adaptive adjustment mechanism. To avoid excessive illegal individuals, an illegal solution adjustment mechanism is added to the GNF-QGA. A solid demonstration was provided that the proposed algorithm has a fast convergence speed and good optimization capability when solving network coding resource optimization problems.

FLRED: an efficient fuzzy logic based network congestion control method

2016 ◽  
Vol 30 (3) ◽  
pp. 925-935 ◽  
Author(s):  
Mosleh M. Abualhaj ◽  
Ahmad Adel Abu-Shareha ◽  
Mayy M. Al-Tahrawi
Keyword(s):  
Fuzzy Logic ◽  
Congestion Control ◽  
Control Method ◽  
Network Congestion ◽  
Network Congestion Control

TEST DATA GENERATION FOR SOFTWARE TESTING BASED ON QUANTUM-INSPIRED GENETIC ALGORITHM

2013 ◽  
Vol 12 (01) ◽  
pp. 1350004 ◽  
Author(s):  
CHENGYING MAO ◽  
XINXIN YU
Keyword(s):  
Genetic Algorithm ◽  
Software Testing ◽  
Test Data ◽  
Optimal Solution ◽  
Search Space ◽  
Structural Testing ◽  
Test Data Generation ◽  
Data Generation ◽  
Quantum Bit

The quality of test data has an important impact on the effect of software testing, so test data generation has always been a key task for finding the potential faults in program code. In structural testing, the primary goal is to cover some kinds of structure elements with some specific inputs. Search-based test data generation provides a rational way to handle this difficult problem. In the past, some well-known meta-heuristic search algorithms have been successfully utilized to solve this issue. In this paper, we introduce a variant of genetic algorithm (GA), called quantum-inspired genetic algorithm (QIGA), to generate the test data with stronger coverage ability. In this new algorithm, the traditional binary bit is replaced by a quantum bit (Q-bit) to enlarge the search space so as to avoid falling into local optimal solution. On the other hand, some other strategies such as quantum rotation gate and catastrophe operation are also used to improve algorithm efficiency and quality of test data. In addition, experimental analysis on eight real-world programs is performed to validate the effectiveness of our method. The results show that QIGA-based method can generate test data with higher coverage in much smaller convergence generations than GA-based method. More importantly, our proposed method is more robust for algorithm parameter change.

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