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When solving the routing problem with traditional ant colony algorithm, there is scarce in initialize pheromone and a slow convergence and stagnation for the complex network topology and the time-varying characteristics of channel in power line carrier communication of low voltage distribution grid. The algorithm is easy to fall into premature and local optimization. Proposed an automatic network algorithm based on improved transmission delay and the load factor as the evaluation factors. With the requirements of QoS, a logical topology of power line communication network is established. By the experiment of MATLAB simulation, verify that the improved Dynamic hybrid ant colony genetic algorithm (DH_ACGA) algorithm has improved the communication performance, which solved the QoS routing problems of power communication to some extent.

LV-PLC (Low-voltage power line carrier communication) technology has become the key and difficult technology for smart grid development [

The traditional Ant Colony System (ACS) adopts positive feedback and adaptive mechanism to accelerate the convergence speed of the algorithm, but the algorithm is easy to fall into local optimal [

Starting from the reality of the power communication network topology, this paper simulates the establishment of a logical communication network. Because the algorithm was affected by the density of nodes in the network, communication efficiency, communication distance and other conditions, and taking into account the number of hops, bandwidth, delay, error rate, packet loss rate, interference and other indicators [

The logic topology of power line communication network was designed based on a tree and star network, as shown in

Select a power line communication network covering an area of 240 × 240 m^{2} in a certain smart grid, and randomly arrange 50 nodes on its secondary side, as shown in

In the real test case, the link connectivity of power line communication network data is very limited in scope. It is not only related to the equipment used in network communication, network load, channel status and other factors, but also related to the degree of the node and the effective communication distance. And when the nodes change, the effects on the logical topology of the entire network is also great.

Set m for the numbers of ant, and n for the number of network communication. The distance between node i and j is d i j ( i , j = 1 , 2 , ⋯ , n ) . τ i j ( t ) is the pheromone concentrations on the connection path between node i and j at the time of t. Initially, the pheromone concentrates on connectivity path is same, so set the τ i j ( 0 ) = τ 0 .

In the genetic algorithm, the design of fitness function is usually a combination of constraints and the cost function based on the need to meet to construct. But for multi-constrained Quality of Service (QoS) routing, its fitness function can generally be described as [

F ( z ) = φ 1 ⋅ g d e l a y ( z ) + φ 2 ⋅ h l o a d ( z ) . (1)

where, φ_{1}, φ_{2} is the weight of the function, and φ 1 + φ 2 = 1 . It can be adjusted by the values of these two parameters to vary the proportion of the delay and the load node. g d e l a y ( z ) is delay function for the network, and its expression is as follows,

g d e l a y ( z ) = d e l a y max − d e l a y ( Z ( s , d ) ) d e l a y max . (2)

where,

d e l a y ( Z ( s , d ) ) = ∑ e ∈ Z ( s , d ) d e l a e y ( e ) . (3)

In Equations (9) and (10), delay_{max} is the maximum network delay, Z ( s , d ) is the network path to the source node s to the destination node d, delay(e) is the delay of one link e from the source node s to the destination node d, and h l o a d ( z ) is the load factor of the nodes in the system.

h l o a d ( i ) = 1 λ i . (4)

where, λ_{i} is the number of nodes in the network during the transmission of information to be forwarded.

Most existing algorithms based on the number of paths to optimize the factors used to evaluate the objective function. But this complex network of distribution network is needed to consider the transmission delay, load balancing, QoS routing algorithm running time, and other factors [

{ min ( φ 1 ⋅ g d e l a y ( z ) + φ 2 ⋅ h l o a d ( z ) ) s . t { | Z ( i ) − Z ( j ) | ≤ 2 d e l a y ( Z ( s , d ) ) ≤ d e l a y max h l o a d ( z ) ≤ h max Z = { z 1 , z 2 , ⋯ , z N } (5)

where, i and j represent two adjacent nodes. Z represents the set of carrier nodes of the entire network. N is the number of nodes. | Z ( i ) − Z ( j ) | is the distance between two adjacent nodes. The minimum communication distance in the network must be not less than two to ensure communication reliability.

In the case of range of low voltage power line communications network nodes and the complex topology of network, it will undoubtedly lead to information transfer delay, resulting in reduced efficiency of the algorithm processing. The genetic operators are joined in the late of ant colony algorithm, using an improved crossover operator and the mutation operator. after each of iteration, the paths are crossed and mutated, using individual fitness function to select [

In order to improve the operating efficiency of the algorithm, proposing the fusion conditions of ant colony algorithm and genetic algorithm, and control the switching of iterations and the end of the algorithm through the transmission delay and algorithms. The number of iterations of ant colony algorithm is N_{a}. Ant colony algorithm will switch to genetic algorithm at the time of Na ≥ Na_{max}, Na_{max} is maximum number of iterations. Conditions for the end of the whole algorithm system are as follows,

{ N g > N g max d d e l a y ( Z ( s , d ) ) > d e l a y max (6)

where, N_{g}, Ng_{max}, d_{delay}(Z(s, d)), and delay_{max} is the iterations of genetic algorithm, the maximum number of iterations of genetic algorithm, system information transmission delay, and the maximum propagation delay, respectively. When the above conditions are satisfied, output optimal path and end the algorithm. The flow of DH_ACGA shows in

Combined with the logical topology of power line carrier communication network in low voltage distribution network, consider from a fast algorithm, robustness, convergence angles, DH_ACGA algorithm is simulated by MATLAB programming. Test and compare the DH_ACGA algorithm and the ACS algorithm on a simulation model close to the real environment. According to the results of the simulation results, the feasibility of DH_ACGA algorithm is verified.

Using the logical topology of communication networks in

The balance of network load is an important indicator of the reliability of a network. It can be seen from the simulation results in

Parameter names | Parameter setting | Parameter meanings |
---|---|---|

n | 50 | The number of nodes in net |

α | 1 | Weighting factor of pheromone |

β | 1 | Inspired factor of pheromone |

q_{0} | 0.5 | The probability threshold of path searching |

ρ | 0.02 | The global pheromone volatilization coefficient |

τ_{0} | 10 | Initial value of pheromone |

ξ | 0.04 | The local pheromone volatilization coefficient |

Q | 100 | Control parameters |

φ_{1} | 0.6 | The delay weight factor of objective function |

φ_{2} | 0.4 | The load weight factor of objective function |

Na_{max} | 50 | The maximum number of iterations in ACS |

According to the time complexity and space complexity of the algorithm performance evaluation, the experiment was repeated 50 times respectively. And the statistics of the average convergence value, average convergence iteration number, convergence value, average success rate and calculation time of each algorithm are calculated respectively. The experimental results shown in

As can be seen from

Algorithm | The number of nodes in the optimal solution | The average convergence value | The average convergence iterations | Optimal convergence value (Optimal value: 0.6055) | The average running time of the algorithm (s) |
---|---|---|---|---|---|

ACS | 8 | 0.6702 | 9 | 0.6811 | 8.35 |

DH_ACGA | 5 | 0.6010 | 14 | 0.6055 | 5.26 |

In this paper, a DH_ACGA algorithm based on delay and load as evaluation factors is proposed on the basis of considering factors such as delay, objective function value, node load, running time, hop count, etc. And compared with the ACS algorithm for simulation and analysis, the following conclusions can be drawn:

1) In the same topology of communications network, analyzing the load balancing of two algorithms, DH_ACGA algorithm can well balance the load of nodes.

2) According to the logical topology of communication nodes in network, sending several “Ant” and setting a target node, DH_ACGA algorithm with a small number of iterations to find the destination node. In the situation of network node failure, information can still reach the target node and has a good self-healing capability of network.

From the simulation analysis of the experiment can prove DH_ACGA algorithm is applicable to LV-PLC network, and be able to provide satisfactory QoS routing services. However, the algorithm also has shortcomings, there is a high complexity degree of time and space. Meanwhile, LV-PLC technology is also faced with the harsh environment, network complexity, noise pollution, electromagnetic interference, and other issues. These issues may lead to lower communication quality and reliability, thus posing a huge challenge to LV-PLC technology.

The authors declare no conflicts of interest regarding the publication of this paper.

Xiao, Q.H., Jin, H. and Zhang, X.Y. (2020) Power Line Communications Networking Method Based on Hybrid Ant Colony and Genetic Algorithm. Engineering, 12, 581-590. https://doi.org/10.4236/eng.2020.128040