Multicast routing optimization method based on quantum evolution

Abstract

The invention discloses a multicast routing optimization method based on quantum evolution, relating to the evolutionary computation field and aiming to solve the problems of long optimization time, easy convergence to local optimum, and the like existing in the prior art. The multicast routing optimization method based on quantum evolution comprises the following steps: 1. generating a stochastic network and setting operational parameters; 2. solving all alternative paths satisfying time-delay conditions for each destination node; 3. carrying out quantum coding on the alternative path set of each destination node so as to acquire a state matrix; 4. carrying out quantum observation on the state matrix so as to obtain a set of binary strings; 5. decoding the binary strings so as to obtain the selected path of each destination node, and computing a multicast tree fitness function; 6. carrying out quantum variation on the state matrix by a quantum revolution door; 7. obtaining a set of new binary strings by observing the state matrix, and then computing a new multicast tree fitness function after the new binary strings are decoded. The invention has low complexity of computation and low cost of the optimized multicast tree and can be used for effectively allocating network resources.

Description

technical field [0001] The invention belongs to the field of network communication calculation and relates to a multicast routing method, which can be used to improve the service quality of the Internet. Background technique [0002] From the end of the last century to the beginning of this century is the era of great development of network technology, especially the IP network represented by the Internet is changing with each passing day. The number of users on the Internet continues to grow explosively, and the applications on the Internet have shifted from traditional e-mail to FTP, WWW and other applications. In addition, new Internet-based applications and services are constantly being launched, such as e-commerce, IP telephony, and video conferencing. However, it is difficult to meet the requirements of these services, especially to ensure the special requirements of some real-time services such as bandwidth and delay, with the current best-effort service in the Inter...

AI Technical Summary

Problems solved by technology

The disadvantages of this method are: high complexity; it may not find the optimal solution when the solution exists

The disadvantage of this method is that the solution process is complicated, and it is not suitable for optimizing large-scale networks.

[0005] In recent years, the method based on the genetic method to solve the multicast routing has enhanced the global search ability compared with the classical method, but it is easy to fall into "premature", and it is difficult to obtain the optimal multicast tree.

However, the method of solving multicast routing based on the artificial immune theory needs to maintain the diversity of the population and obtain the optimal multicast tree through continuous cologne populations, but this greatly reduces the timeliness of the method

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