Multi-agent multicast routing method based on adjacent immune clonal selection

Abstract

The invention discloses a multi-agent multicast routing method based on adjacent immune clonal selection, and mainly aims to overcome the shortcomings of low convergence rate and low searching capability of the conventional method when multicast routing problems are solved. The method is implemented by the following steps of: 1, generating a network model; 2, initializing antibody populations, memory unit populations and optimized running parameters; 3, calculating the affinities of all antibodies, finding an optimal antibody and extracting a vaccine; 4, judging whether termination conditionsare met or not, outputting an optimal individual if the termination conditions are met, otherwise turning to the step 5; 5, performing an immune colonization operation on all individuals in a currentpopulation; 6, performing an agent adjacent competition operation on the population obtained by the step 5, and updating the current population; and 7, extracting a better antibody updating memory unit from the antibody population obtained by the step 6, finding the optimal individual and returning to the step 4. The method has the advantages of high convergence rate and high searching capability, and can be used for solving the multicast routing problems of delay limitations.

Description

technical field [0001] The invention belongs to the technical field of network communication, relates to the application of multi-agent technology in the multicast routing problem, and is used to solve the quality of service (QoS) multicast routing problem, and the better multicast tree obtained by the method has a more reasonable configuration Internet resources. Background technique [0002] With the rapid development of computer networks, network functions are increasingly powerful. The role of the network has evolved from simple information transmission to distance teaching, video conferencing, data distribution, and online games. To send user data from one terminal to another, the transmission route must first be determined. Different communication methods, which determine the route The way is also different. Today's network communication methods mainly include the following types: 1) point-to-point unicast communication; 2) multicast communication that sends informat...

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Problems solved by technology

Zhong Weicai proposed a multi-agent evolutionary method for dynamically expanding the search space in "Multi-Agent Evolutionary Algorithms for Combinatorial Optimization". Only suitable for a specific network, often limited to local optimum, it is difficult to obtain a multicast tree with the least cost, and this method is difficult to implement in parallel

Liu Yuan and others proposed the MAICSA method in "Multi_Agent Multicast Routing Algorithm Based on Immune Cloning Computation". This method first builds a network model to find an optimal transmission path that meets various QoS requirements. It uses a single agent as a network The nodes of the model, and the positions of the agents generated by each generation in the grid are not fixed, and a high number of iterations is required to obtain the multicast tree with the lowest cost, which cannot well meet the requirements of decision makers for rational allocation of network resources

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