Intelligent collaborative path optimization algorithm-based path optimization method and system without dedicated communication channel

Abstract

The application discloses a path optimization method and system without a special communication channel based on an intelligent cooperative path optimization algorithm, and relates to the technical field of communication networks.In the path optimization process, factors such as communication reliability index, relay node number and load balance degree are comprehensively considered, so that the selected path not only meets the low delay requirement, but also maintains high reliability under different network states, avoids the path instability problem caused by network fluctuations, and ensures the stability and robustness of communication.Through twice fusion of the intelligent path optimization algorithm and the genetic algorithm, the respective advantages of the two algorithms are fully brought into play.The global search capability of the ant colony algorithm and the local search capability of the genetic algorithm are complementary, so that in the path optimization process, the local optimal solution can be effectively avoided, the high efficiency of calculation can be ensured, and the overall path optimization performance is enhanced.

Description

Technical Field

[0001] This invention relates to the field of communication network technology, and more specifically to a method and system for optimizing paths without dedicated communication channels based on intelligent cooperative path optimization algorithms. Background Technology

[0002] Intelligent path algorithms exhibit good adaptability in solving networking problems without dedicated communication channels. Based on a positive feedback mechanism, the algorithm converges to the optimal path. However, intelligent path algorithms inherently have limitations; they are prone to getting trapped in local optima during networking. Traditional intelligent path algorithms primarily find an optimal path that searches all nodes from a given starting point, forming a closed path. The objective function is the distance of the evaluated path, but the network setup process does not require a direct return from the starting point to the end point. Therefore, to avoid excessive network communication time due to overload at a single node, and to ensure that the optimal path found by the algorithm better meets communication quality requirements, it is necessary to improve traditional intelligent path algorithms. This involves optimizing the pheromone update rules and path transition rules to increase path search speed and reduce the required time in power line networking. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for optimizing paths without dedicated communication channels based on an intelligent collaborative path optimization algorithm, aiming to solve problems such as communication delays caused by excessive relay nodes due to unreasonable path selection.

[0004] The present invention achieves the above objectives through the following technical solutions:

[0005] A path optimization method without a dedicated communication channel based on an intelligent collaborative path optimization algorithm, the method comprising:

[0006] The physical distance between nodes is determined based on the topology of the initialized network, the injected grid power and the number of relay nodes are set, and a dynamic weight allocation transfer function without a dedicated communication channel is constructed based on the physical distance, injected grid power and the number of relay nodes.

[0007] Based on the weight allocation result of the transfer function without a dedicated communication channel, the objective function of the ant colony algorithm is improved. The number of relay nodes, the communication reliability index, and the load balance degree are selected as the evaluation factors of the improved objective function to obtain the intelligent path optimization algorithm. The pheromone update rule and path transfer rule of the intelligent path optimization algorithm are then optimized.

[0008] Based on the intelligent path optimization algorithm, a preliminary path solution is generated. The preliminary path solution is used as the initial population of the genetic algorithm for iterative optimization. According to the fitness function of the genetic algorithm, a high-quality individual is selected as the parent generation by a proportional selection operator. Mutation and crossover operations are performed based on the dynamic node number encoding rule. The fitness function value of each path is calculated to generate a global optimization solution, thus completing the first fusion of the intelligent path optimization algorithm and the genetic algorithm.

[0009] The global optimization solution is fed back to the pheromone distribution update of the intelligent path optimization algorithm, completing the second fusion of the intelligent path optimization algorithm and the genetic algorithm to obtain the intelligent cooperative path optimization algorithm.

[0010] Based on the aforementioned intelligent collaborative path optimization algorithm, collaborative iterative optimization is performed to dynamically adjust the path search direction and output the optimal communication path.

[0011] As a preferred embodiment of the present invention, the transfer function without a dedicated communication channel is expressed as follows:

[0012]

[0013] In the formula, H(f) is the transfer function without a dedicated communication channel; l represents the path number, and N is the number of transmissions along the path; g l d represents the weighting coefficient for path l. l P is the physical distance of path l. l To inject power into the grid; N relay ξ is the number of relay nodes in the path; α and β are adjustment parameters; k is the attenuation coefficient; ξ r is the reflection coefficient; f is the signal frequency; j is the imaginary part of the complex number.

[0014] In a preferred embodiment of the present invention, the objective function of the intelligent path optimization algorithm consists of an algebraic formula for the fitness of the function and constraints on the limiting function. The constraints are the number of relay nodes, the communication reliability index, and the load balance. The algebraic formula for the fitness of the function is expressed as follows:

[0015]

[0016] In the formula, cost(L) represents the cost of path L; ψ(L) represents the load factor of path L; λ1 and λ2 are the weights of the cost cost(L) and load factor ψ(L) of path L, respectively; l1, l2…l N These represent the 1st, 2nd, ..., Nth paths, respectively; the communication distance between two adjacent nodes is defined as 1.

[0017] As a preferred embodiment of the present invention, the fitness function of the intelligent path optimization algorithm includes:

[0018] cost(L(i,m))=γ delay (L(i,m))·ω delay +γ loss (L(i,m))·ω loss ;

[0019]

[0020] In the formula, cost(L(i,m)) represents the cost of the path L connecting node i to node m; γ delay (L(i,m)) represents the sum of the number of relay nodes in the path L connecting node i to node m; γ loss (L(i,m)) represents the cumulative effect of the communication reliability index of all links in path L from node i to node m; ω delay ω loss represents the weighting coefficients for the number of relay nodes and the communication reliability index, respectively; a represents a single link on path L(i,m); delay(a) represents the number of relay nodes required for link a; loss(a) represents the communication reliability index of link a.

[0021] As a preferred embodiment of the present invention, the pheromone update rule of the intelligent path optimization algorithm includes global pheromone update and local pheromone update, wherein the formula for global pheromone update is:

[0022] τ im (t+1)=(1-ρ)·τ im (t)+ρ·Δτ im (t);

[0023]

[0024] In the formula, τ im (t+1) represents the pheromone concentration along the path from node i to node m at time t+1; τ ij (t) represents the pheromone concentration along the path from node i to node m at time t; ρ represents the pheromone volatility influencing factor; Δτ im (t) represents the total pheromone concentration released by all ants on the path from node i to node m at time t; cost(L(i,m)) represents the cost of the path L from node i to node m; Q is a constant representing the total pheromone released by the ants after visiting all nodes;

[0025] The formula for updating the local pheromone is:

[0026] τ im (t+1)=(1-μ)·τ im (t)+μ·τ im (0);

[0027] In the formula, μ represents the local pheromone evaporation coefficient; τ im (0) represents the initial value of the pheromone.

[0028] As a preferred embodiment of the present invention, the path transition rules of the intelligent path optimization algorithm are optimized as follows: Signal strength and path distance are used together as heuristic factors, and the node closest to the current node is selected within an allowed range when choosing the next hop node, as shown in the formula:

[0029]

[0030] In the formula, η im (t) represents the transition probability from node i to node m at time t+1; Signal im (t) represents the signal strength at node m on the path from node i to node m at time t; K represents a constant coefficient of signal strength; d im This represents the physical distance between node i and node m.

[0031] As a preferred embodiment of the present invention, the fitness function of the genetic algorithm is defined as:

[0032]

[0033] In the formula, f is the fitness value of each path; cost(L(i,m)) represents the cost of the path L connecting node i to node m.

[0034] In a preferred embodiment of the present invention, the formula for calculating the path probability in the proportional selection operator is as follows:

[0035]

[0036] In the formula, P l f represents the probability of an individual being selected in a genetic algorithm. l is the fitness value of path l; N is the number of transmissions along the path.

[0037] As a preferred embodiment of the present invention, the dynamic node sequence number encoding rule is specifically as follows:

[0038] Based on the characteristics of power line carrier communication data transmission system networks, and following the coding rules of genetic algorithms, Arabic numerals are used to uniquely encode the master node and other nodes.

[0039] Path coding is represented by a chromosome structure, which consists of a sequence of path nodes from the initial node to the target node. Each number in the path node sequence corresponds to a unique node identifier in the communication network. The node identifiers passed through in the path are connected in the transmission order to form a chain coding structure that represents the complete communication path topology.

[0040] A path optimization system based on an intelligent cooperative path optimization algorithm and a path optimization method without a dedicated communication channel, the system comprising:

[0041] The transfer function construction module is used to determine the physical distance between nodes based on the topology of the initialized network, set the injected grid power and the number of relay nodes, and construct a dynamic weight allocation transfer function without a dedicated communication channel based on the physical distance, injected grid power and the number of relay nodes.

[0042] The intelligent path optimization module is used to improve the objective function of the ant colony algorithm. The number of relay nodes, communication reliability index and load balance are selected as evaluation factors of the improved objective function to obtain the intelligent path optimization algorithm.

[0043] The rule optimization module is used to optimize the pheromone update rules and path transition rules of the intelligent path optimization algorithm;

[0044] The algorithm fusion module generates preliminary path solutions based on the intelligent path optimization algorithm, uses the preliminary path solutions as the initial population of the genetic algorithm for iterative optimization, selects high-quality individuals as parents by proportional selection operators according to the fitness function of the genetic algorithm, performs mutation and crossover operations based on the dynamic node number encoding rules, calculates the fitness function value of each path, generates a global optimization solution, and completes the first fusion of the intelligent path optimization algorithm and the genetic algorithm.

[0045] The intelligent collaborative path optimization module is used to feed back the global optimization solution to the pheromone distribution update of the intelligent path optimization algorithm, thereby completing the second fusion of the intelligent path optimization algorithm and the genetic algorithm to obtain the intelligent collaborative path optimization algorithm.

[0046] The result output module is used to perform collaborative iterative optimization based on the intelligent collaborative path optimization algorithm, dynamically adjust the path search direction, and output the optimal communication path.

[0047] A new path evaluation function is introduced to make the optimal path found by the algorithm more in line with the requirements of communication quality. The pheromone update rules and path transfer rules in the intelligent path optimization algorithm are optimized, which improves the path search speed of the algorithm in the process of power line networking and reduces the time required. The ant colony algorithm and the genetic algorithm are integrated to accelerate the iteration speed of each path search and expand the search range in the optimization process. Attached Figure Description

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0049] in:

[0050] Figure 1 This is a flowchart of the method of the present invention;

[0051] Figure 2 This is a schematic diagram of the method flow in an embodiment of the present invention;

[0052] Figure 3 This is a schematic diagram of the modular structure of the system of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0054] like Figure 1 As shown, this is an embodiment of the present invention, which provides a path optimization method without a dedicated communication channel based on an intelligent cooperative path optimization algorithm, including the following:

[0055] S1: Network Initialization

[0056] This paper proposes a transfer function for optimizing the number of relay nodes and the power injected into the power grid without a dedicated communication channel. Taking power line carrier communication as an example, this paper considers the characteristics of the transmission system network structure, avoids power grid harmonic interference caused by excessive node load, and avoids communication delay caused by too many relay nodes due to unreasonable path selection. Different weights are assigned to different paths and nodes according to the actual physical conditions and the importance of the load, so as to reduce the use of relay nodes.

[0057] Specifically, the physical distance between nodes is determined based on the network topology, the injected grid power and the number of relay nodes are set, and a dynamic weight allocation transfer function without a dedicated communication channel is constructed based on the physical distance, injected grid power and the number of relay nodes for subsequent weight allocation and path optimization.

[0058] The transfer function without a dedicated communication channel is expressed as:

[0059]

[0060] In the formula, H(f) is the transfer function without a dedicated communication channel; l represents the path number, and N is the number of transmissions along the path; g l d represents the weighting coefficient for path l. l P is the physical distance of path l. l The injected power represents the grid harmonic interference caused by the node loads on path l; N relay ξ is the number of relay nodes in the path; α and β are adjustment parameters used to balance the impact of injected grid power and the number of relay nodes on the transfer function; k is the attenuation coefficient; ξ r is the reflection coefficient, which reflects the reflection of the signal in the transmission medium; f is the signal frequency; j is the imaginary part of the complex number.

[0061] The purpose of the transfer function is to quantify the transmission performance of a path, including factors such as physical distance, grid power, and the number of relay nodes. This allows path optimization to comprehensively consider multiple factors, thereby achieving a better path selection. The design of the transfer function provides a weighting system for the path optimization algorithm, which optimizes path selection by dynamically adjusting various parameters, ensuring the efficiency and reliability of the communication path under different conditions.

[0062] S2: Ant Colony Algorithm Optimization

[0063] Based on the weight allocation results of the transfer function without a dedicated communication channel, the objective function of the ant colony algorithm is improved. The number of relay nodes, the communication reliability index, and the load balance degree are selected as evaluation factors for the improved objective function, resulting in an intelligent path optimization algorithm. The pheromone update rule and path transfer rule of the intelligent path optimization algorithm are also optimized.

[0064] The optimized intelligent path optimization algorithm can take into account multiple performance indicators during the optimization process, thereby improving the effectiveness of path optimization and the stability of the communication system.

[0065] S21: The objective function of the intelligent path optimization algorithm consists of an algebraic formula for the function fitness and constraints on the limiting function. The constraints are the number of relay nodes, the communication reliability index, and the load balance, replacing the previous method that relied solely on "hop count" for objective optimization. Considering the cost and load balancing during the algorithm's optimization process, the algebraic formula for the function fitness is expressed as:

[0066]

[0067] In the formula, cost(L) represents the cost of path L; ψ(L) represents the load factor of path L; λ1 and λ2 are the weights of the cost cost(L) and load factor ψ(L) of path L, respectively; l1, l2…lN These represent the 1st, 2nd, ..., Nth paths, respectively; the communication distance between two adjacent nodes is defined as 1.

[0068] Considering the high noise, time-varying nature, and decay of the fitness function, the fitness function of the intelligent path optimization algorithm includes:

[0069] cost(L(i,m))=γ delay (L(i,m))·ω delay +γ loss (L(i,m))·ω loss ;

[0070]

[0071] In the formula, cost(L(i,m)) represents the cost of the path L connecting node i to node m; γ delay (L(i,m)) represents the sum of the number of relay nodes in the path L connecting node i to node m; γ loss (L(i,m)) represents the cumulative effect of the communication reliability index of all links in path L from node i to node m; ω delay ω loss represents the weighting coefficients for the number of relay nodes and the communication reliability index, respectively; a represents a single link on path L(i,m); delay(a) represents the number of relay nodes required for link a; loss(a) represents the communication reliability index of link a.

[0072] The objective function is designed to balance multiple evaluation criteria for paths (such as cost and load). Constraints ensure that the optimized path meets communication performance requirements while avoiding overly complex or unstable path configurations, thus ensuring the multi-objective optimization function of the path optimization algorithm and preventing optimization from considering only a single factor. The fitness function evaluates the merits of each path by comprehensively considering various path parameters, thereby guiding the algorithm to select the optimal path.

[0073] S22: By adaptively improving the intelligent path optimization algorithm, optimization is carried out in two aspects: pheromone update and next-hop node transfer rules.

[0074] Optimize the pheromone update rules of the intelligent path optimization algorithm, including global pheromone update and local pheromone update;

[0075] The formula for global pheromone update is:

[0076] τ im (t+1)=(1-ρ)·τ im (t)+ρ·Δτ im (t);

[0077]

[0078] In the formula, τ im (t+1) represents the pheromone concentration along the path from node i to node m at time t+1; τ ij (t) represents the pheromone concentration along the path from node i to node m at time t; ρ represents the pheromone volatility influencing factor; Δτ im (t) represents the total pheromone concentration released by all ants on the path from node i to node m at time t; cost(L(i,m)) represents the cost of the path L from node i to node m; Q is a constant representing the total pheromone released by the ants after visiting all nodes;

[0079] The global pheromone update formula consists of two parts: the pheromone remaining on the path at time t and the amount of pheromone added during the current optimization process. The initial pheromone increase is 0, i.e., Δτ. im (0) = 0.

[0080] The formula for local pheromone updates is:

[0081] τ im (t+1)=(1-μ)·τ im (t)+μ·τ im (0);

[0082] In the formula, μ represents the local pheromone evaporation coefficient; τ im (0) represents the initial value of the pheromone.

[0083] The path transition rules of the optimized intelligent path optimization algorithm are improved by incorporating signal strength as another heuristic factor influencing the function. In the specific design, when selecting the next-hop transition node, each node chooses a node closer to it within an acceptable range. This ensures the signal strength of the selected path while also considering the impact of the channel.

[0084] Specifically, the signal strength and path distance are used as heuristic factors. When selecting the next-hop node, the node closest to the current node is chosen within the allowed range. The formula is as follows:

[0085]

[0086] In the formula, η im (t) represents the transition probability from node i to node m at time t+1, that is, the tendency of node i to choose node m when selecting the next hop node, η im The higher the value of (t), the more likely node i is to choose node m as its next hop;

[0087] Signal im (t) represents the signal strength at node m on the path from node i to node m at time t; K represents a constant coefficient of signal strength; d im This represents the physical distance between node i and node m.

[0088] S3: Genetic Algorithm Optimization

[0089] Similar to intelligent path optimization algorithms, genetic algorithms also include parameters related to the number of relay nodes, communication reliability index, and load balancing in their individual fitness functions. Fewer relay nodes, a higher communication reliability index, and a higher load balancing indicate a higher fitness for the path, making it more likely to meet the requirements of an optimal path. Conversely, a lower fitness and poorer performance indicate a path unsuitable for selection as a "parent" population for replication in the algorithm.

[0090] The fitness function of a genetic algorithm is defined as:

[0091]

[0092] In the formula, f is the fitness value of each path; cost(L(i,m)) represents the cost of the path L connecting node i to node m.

[0093] S4: First Algorithm Fusion

[0094] The initial path solution is generated based on the intelligent path optimization algorithm. The initial path solution is used as the initial population of the genetic algorithm for iterative optimization. According to the fitness function of the genetic algorithm, high-quality individuals are selected as parents by the proportional selection operator. Mutation and crossover operations are performed based on the dynamic node number encoding rule. The fitness function value of each path is calculated to generate the global optimal solution, thus completing the first fusion of the intelligent path optimization algorithm and the genetic algorithm.

[0095] The proportional selection operator, also known as roulette wheel selection, plays the role of "referee" in intelligent path optimization algorithms and genetic algorithms. By using the proportional selection operator, or roulette wheel selection, it ensures that the selected path considers not only power grid harmonic interference but also the number of relay nodes, communication reliability index, and load balancing, thereby improving path search speed and optimization effectiveness. When applied to genetic algorithms, to ensure global convergence, the optimal individual is retained as a further selection operator.

[0096] The formula for calculating the path probability in the proportional selection operator is:

[0097]

[0098] In the formula, P l f represents the probability of an individual being selected in a genetic algorithm. lis the fitness value of path l; N is the number of transmissions along the path; This represents the sum of the fitness functions of individuals in a genetic algorithm.

[0099] The specific rules for dynamic node number encoding are as follows:

[0100] Based on the characteristics of power line carrier communication data transmission system networks, and following the coding rules of genetic algorithms, Arabic numerals are used to uniquely encode the master node and other nodes.

[0101] Path coding is represented by a chromosome structure, which consists of a sequence of path nodes from the initial node to the target node. Each number in the path node sequence corresponds to a unique node identifier in the communication network. The node identifiers passed through in the path are connected in the transmission order to form a chain coding structure that represents the complete communication path topology.

[0102] Each path formed can yield a string of chromosomes. For example, if the initial node needs to search for the target node 8, and during the algorithm's operation, the information needs to pass through nodes 6, 10, and 4 to finally reach the target node 8, then the resulting path code is denoted as: 1-6-10-4-8.

[0103] S5: Second Algorithm Fusion

[0104] The global optimization solution is fed back to the pheromone distribution update of the intelligent path optimization algorithm, completing the second fusion of the intelligent path optimization algorithm and the genetic algorithm, resulting in an intelligent cooperative path optimization algorithm. This improves the path search speed and reduces the required time during the networking process without a dedicated communication channel.

[0105] S6: Collaborative Iterative Optimization

[0106] Based on the intelligent collaborative path optimization algorithm, collaborative iterative optimization is performed to dynamically adjust the path search direction and output the optimal communication path that meets the requirements of low latency, high reliability and balanced load.

[0107] For example, in a distributed smart grid environment, multiple nodes need to transmit information through optimal paths. By initializing the network topology, setting the distance between nodes, grid power, and the number of relay nodes, a transfer function without dedicated communication channels is constructed. An improved ant colony algorithm is used to optimize path selection, combined with a genetic algorithm for iterative optimization, ultimately selecting a communication path that satisfies low latency, high reliability, and load balancing.

[0108] For example, in an intelligent transportation system within a city, multiple traffic monitoring nodes need to transmit real-time data. The intelligent cooperative path optimization algorithm in this method first determines the physical distance and transmission requirements between nodes. Then, by leveraging the synergistic effect of the improved intelligent path optimization algorithm and the genetic algorithm, the data transmission path is optimized, ensuring data transmission under conditions of low latency, high reliability, and load balancing, thereby improving the efficiency of the overall traffic management system.

[0109] like Figure 2 The figure shows a specific embodiment of the present invention, which provides one specific implementation process of a path optimization method without a dedicated communication channel based on an intelligent cooperative path optimization algorithm.

[0110] Network Initialization: Upon system startup, the physical distances between nodes are first determined through network initialization steps. Appropriate grid power injection and the number of relay nodes are then set according to network requirements. After initialization, the system prepares for path optimization calculations. Assume an iteration count of 10, a grid power injection of 100 watts, and 3 relay nodes.

[0111] Path selection and optimization process: The next node is selected according to the path transition rules, and the evaluation factors of the objective function are calculated. These evaluation factors include:

[0112] Number of relay nodes: The number of relay nodes involved in the path.

[0113] Communication reliability index: The communication quality of a path, which is usually related to link quality and network status.

[0114] Load balance: The load balance of the paths, reflecting the load situation of each path.

[0115] At this point, the path evaluation factors show that the current path has a communication reliability index of 0.85, a relay node count of 2, a load balance of 0.7, and an objective function value of 1.2.

[0116] Constraint Check and Node Update: Check if the current path meets the constraints (e.g., whether the maximum number of nodes has been reached). If the current path meets the constraints, determine whether the target node has been reached. If not, continue with path selection and information update, and continue path selection. Update the local pheromone based on the objective function evaluation results, for example, adjust the number of relay nodes, and recalculate the fitness of each path.

[0117] Path Iterative Optimization: The search direction of the path is dynamically adjusted through an iterative optimization process. For example, the direction of node selection is adjusted or a new search strategy is used to select the next hop node. Assume the system updates the path based on local information and selects the node with the shortest distance and highest signal strength as the next hop node.

[0118] Intelligent path optimization is integrated with genetic algorithms: Genetic algorithms are used to optimize the generated initial path solutions. First, a fitness function is used to evaluate the quality of each path, selecting the path with higher fitness as the parent. Based on dynamic node indexing rules, path mutation and crossover operations are performed to further improve path performance. After one round of genetic algorithm optimization, the shortest path with good load balancing is selected, generating a globally optimized solution.

[0119] Feedback optimization solution and second fusion: The global optimization solution obtained through the genetic algorithm is fed back to the intelligent path optimization algorithm to update the pheromone distribution. This process completes the second fusion of the intelligent path optimization algorithm and the genetic algorithm, thus obtaining the final intelligent cooperative path optimization algorithm.

[0120] Optimal Path Output: After multiple iterations and optimizations, the system finally outputs an optimal path that meets the requirements of low latency, high reliability, and balanced load. Assuming the final optimized path is within 5 hop nodes, the communication latency is 50ms, the reliability index is 0.95, and the load balance is 0.85, it meets the expected communication requirements.

[0121] Through the optimization process in this embodiment, the system can successfully find the optimal communication path that satisfies low latency, high reliability, and load balancing by combining intelligent path optimization algorithm and genetic algorithm, thereby improving the overall performance of the network.

[0122] like Figure 3 As shown, the path optimization system based on the intelligent cooperative path optimization algorithm and the path optimization method without a dedicated communication channel provided by the present invention includes:

[0123] The transfer function construction module is used to determine the physical distance between nodes based on the topology of the initialized network, set the injected grid power and the number of relay nodes, and construct a dynamic weight allocation transfer function without a dedicated communication channel based on the physical distance, injected grid power and the number of relay nodes;

[0124] The intelligent path optimization module is used to improve the objective function of the ant colony algorithm. The number of relay nodes, communication reliability index and load balance are selected as evaluation factors of the improved objective function to obtain the intelligent path optimization algorithm.

[0125] The rule optimization module is used to optimize the pheromone update rules and path transition rules of the intelligent path optimization algorithm;

[0126] The algorithm fusion module generates preliminary path solutions based on the intelligent path optimization algorithm. These preliminary path solutions are then used as the initial population for the genetic algorithm to iteratively optimize the algorithm. Based on the fitness function of the genetic algorithm, a high-quality individual is selected as the parent generation using a proportional selection operator. Mutation and crossover operations are performed based on the dynamic node number encoding rules. The fitness function value of each path is calculated, and a global optimal solution is generated, completing the first fusion of the intelligent path optimization algorithm and the genetic algorithm.

[0127] The intelligent collaborative path optimization module is used to feed back the global optimization solution to the pheromone distribution update of the intelligent path optimization algorithm, and complete the second fusion of the intelligent path optimization algorithm and the genetic algorithm to obtain the intelligent collaborative path optimization algorithm.

[0128] The results output module is used for collaborative iterative optimization based on the intelligent collaborative path optimization algorithm, dynamically adjusting the path search direction, and outputting the optimal communication path that meets the requirements of low latency, high reliability, and balanced load.

[0129] In summary, this invention, by introducing a collaborative optimization approach combining intelligent path optimization and genetic algorithms, can efficiently find the optimal path in complex network topologies. Through dynamic adjustment and multiple iterative optimizations of path selection, a globally optimal path can be reached in a relatively short time, significantly improving the optimization efficiency of communication paths.

[0130] This invention comprehensively considers factors such as communication reliability index, number of relay nodes and load balance during the path optimization process, so that the selected path not only meets the low latency requirement, but also maintains high reliability under different network conditions, avoids path instability caused by network fluctuations, and ensures communication stability and robustness.

[0131] The transfer function without a dedicated communication channel and its dynamic weight allocation strategy employed in this invention enable the method to flexibly adapt to various scenarios such as different network topologies, network loads, and grid power. By adjusting the weights of various factors, this invention can adjust the path optimization strategy according to actual needs, thus enhancing its adaptability.

[0132] This invention fully leverages the advantages of both intelligent path optimization algorithms and genetic algorithms by fusing them together. The global search capability of the ant colony algorithm and the local search capability of the genetic algorithm complement each other, effectively avoiding local optima while ensuring computational efficiency during path optimization, thus enhancing the overall performance of path optimization.

[0133] This invention optimizes path selection and dynamically adjusts the path search direction, ultimately outputting an optimal communication path that simultaneously satisfies the requirements of low latency and balanced load. This path optimization method effectively avoids common problems in communication networks such as uneven load distribution and excessive latency, thereby improving the overall performance of the communication network.

[0134] Because this invention combines the advantages of intelligent path optimization algorithms and genetic algorithms, and can effectively find the optimal path in complex network environments, it is suitable for path optimization in large-scale network environments. Whether in complex urban communication networks or in wide-area wireless communication environments, this invention can achieve efficient path optimization.

[0135] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for path optimization without dedicated communication channel based on intelligent cooperative path optimization algorithm, characterized in that, The method comprises: Determine the physical distance between nodes based on the topology of the initialized network, set the injected power grid power and the number of relay nodes, and construct a dynamic weight distribution function without dedicated communication channel transmission based on the physical distance, injected power grid power and number of relay nodes; The function without dedicated communication channel transmission is expressed as: ； In the formula, For functions without a dedicated communication channel; Indicates the path number. This represents the number of transmissions along the path. For path The weighting coefficients, For path physical distance, To inject power into the grid; This represents the number of relay nodes in the path. and To adjust the parameters; The attenuation coefficient; The reflection coefficient; The signal frequency; The unit of the imaginary part of a complex number; Based on the weight distribution result of the function without dedicated communication channel transmission, improve the objective function of the ant colony algorithm, select the number of relay nodes, communication reliability index and load balance degree as the evaluation factors of the improved objective function, obtain the intelligent path optimization algorithm, and optimize the pheromone update rule and path transfer rule of the intelligent path optimization algorithm; Optimize the pheromone update rule of the intelligent path optimization algorithm, including global pheromone update and local pheromone update, and the formula of the global pheromone update is: ； ； In the formula, express From the node To the node The concentration of pheromones along the path; express From the node To the node The concentration of pheromones along the path; Factors influencing the volatility of pheromones; express At any given moment, all ants are at the node. To the node The total concentration of pheromones released along the connection path; Indicates from node To the node Connection path The cost; is a constant representing the total amount of pheromones released by the ant after visiting all nodes; The formula of the local pheromone update is: ； wherein represents the volatility coefficient of the local pheromone; represents the initial value of the pheromone; Optimize the path transfer rule of the intelligent path optimization algorithm, specifically: the signal strength and path distance are used as heuristic factors, the node closest to the current node is selected as the next hop node within the allowed range, and the formula is: ； In the formula, express Time Node To the node The transition probability; for From the node To the node Nodes on the path The signal strength; A constant coefficient representing signal strength; Represents a node To the node The physical distance between them; Generate a preliminary path solution based on the intelligent path optimization algorithm, use the preliminary path solution as the initial population of the genetic algorithm for iterative optimization, select high-quality individuals as parents according to the fitness function of the genetic algorithm through the proportional selection operator, perform mutation and crossover operations based on the dynamic node sequence coding rule, calculate the fitness function value of each path, generate a global optimal solution, and complete the first fusion of the intelligent path optimization algorithm and the genetic algorithm; The global optimal solution is fed back to the pheromone distribution update of the intelligent path optimization algorithm, the second fusion of the intelligent path optimization algorithm and the genetic algorithm is completed, and an intelligent collaborative path optimization algorithm is obtained; Based on the intelligent collaborative path optimization algorithm, perform collaborative iterative optimization, dynamically adjust the path search direction, and output the optimal communication path.

2. The method of claim 1, wherein the method is based on an intelligent cooperative path optimization algorithm. The objective function of the intelligent path optimization algorithm is divided into an algebraic formula of function fitness and a constraint condition of limit function, and the constraint condition is the number of relay nodes, the communication reliability index and the load balance degree. The algebraic formula of function fitness is expressed as: ； wherein denotes the cost of a path ; denotes the load factor of a path ; , are the weights of the cost and load factor of a path , respectively; denote the first, second,..., the path, respectively; it is specified that the communication distance between two adjacent nodes is 1. 3.The method of claim 2, wherein, The fitness function of the intelligent path optimization algorithm comprises: ； ； ； wherein denotes the cost of the connection path from node to node ; denotes the sum of the number of relay nodes in all links of the connection path from node to node ; denotes the cumulative effect of the communication reliability indices of all links of the connection path from node to node ; , denote the weighting coefficients of the number of relay nodes and the communication reliability indices, respectively; denotes a single link on the path ; denotes the number of relay nodes required for the link ; denotes the communication reliability index of the link .

4. The method of claim 1, wherein the method is characterized by, The fitness function of the genetic algorithm is defined as: ； wherein is the fitness value for each path; denotes the cost of the connection path from node to node .

5. The method of claim 4, wherein the method further comprises: The calculation formula of the path probability in the proportional selection operator is: ； In the formula, represents the probability of the individual being selected in the genetic algorithm; is the fitness value of the path ; and is the number of transmissions of the path.

6. The intelligent cooperative path optimization algorithm based path optimization method without dedicated communication channel according to claim 1, characterized in that, The dynamic node sequence coding rule is specifically: According to the characteristics of the power line carrier communication data transmission system network, the main node and other nodes are uniquely coded by using Arabic numerals according to the coding rule of the genetic algorithm; The path coding is represented by a chromosome structure, and the chromosome is composed of a path node sequence from an initial node to a target node. Each number in the path node sequence corresponds to a unique node identifier in the communication network. The node identifiers in the path are connected in the transmission order to form a chain coding structure representing the topology of the complete communication path.

7. The path optimization system based on the intelligent cooperative path optimization algorithm without a dedicated communication channel path optimization method according to any one of claims 1-6, characterized in that, The system comprises: The transfer function construction module is configured to determine physical distances between nodes based on a topology structure of an initialized network, set injected grid power and relay node numbers, and construct a dynamic weight distribution transfer function without a dedicated communication channel based on the physical distances, the injected grid power, and the relay node numbers. The intelligent path optimization module is configured to improve an objective function of an ant colony algorithm, select the relay node numbers, a communication reliability index, and a load balance degree as evaluation factors of the improved objective function, and obtain an intelligent path optimization algorithm. The rule optimization module is configured to optimize pheromone update rules and path transfer rules of the intelligent path optimization algorithm. The algorithm fusion module is configured to generate a preliminary path solution based on the intelligent path optimization algorithm, perform iterative optimization on the preliminary path solution as an initial population of a genetic algorithm, select high-quality individuals as parents through a proportional selection operator according to an adaptability function of the genetic algorithm, perform mutation and crossover operations based on a dynamic node sequence number coding rule, calculate an adaptability function value of each path, generate a globally optimized solution, and complete a first fusion of the intelligent path optimization algorithm and the genetic algorithm. The intelligent collaborative path optimization module is configured to feed back the globally optimized solution to pheromone distribution updating of the intelligent path optimization algorithm, complete a second fusion of the intelligent path optimization algorithm and the genetic algorithm, and obtain an intelligent collaborative path optimization algorithm. The result output module is configured to perform collaborative iterative optimization based on the intelligent collaborative path optimization algorithm, dynamically adjust a path search direction, and output an optimal communication path.

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