Abstract
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.
Highlights
In recent years, it has become a research hotspot in the industry to complete the recovery of failures by combining the distributed recovery mechanism of the optical network with optical multicase technology [1,2]
The adaptive quantum genetic algorithm based on gene number and fitness cooperative mutation includes the fitness evaluation mechanism, rotation angle adaptive adjustment mechanism, the cooperative mutation mechanism based on gene number and fitness, and illegal solution adjustment mechanism
Algorithm Procedure The flow of adaptive quantum genetic algorithm based on the cooperative mutation mechPhaotonniicss2m021, 8o, 5f02gene number and fitness is shown in Figure 4, where MAXGEN 8 of 16is the maximum evolutional generation set by the algorithm
Summary
It has become a research hotspot in the industry to complete the recovery of failures by combining the distributed recovery mechanism of the optical network with optical multicase technology [1,2]. Network coding technology has rapidly developed into an important research field in electrical engineering and computer science It has a wide range of applications, such as wireless networks, distributed file storage, and network security, but the encoding operation brings additional computational overhead [12,13]. The quantum genetic algorithm can be used to solve network coding resource optimization problems and has made great progress over the traditional genetic algorithm. On the basis of existing research, and considering the influence of gene number on population variation, an adaptive quantum genetic algorithm (GNFQGA) based on gene number and fitness co-variation is proposed to solve the problem of network coding resource optimization. The experimental data show that the proposed algorithm has a better optimization ability in solving the network coding resource optimization problem
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