Genetic algorithm based minimum-cost optical multicast tree routing method

Abstract

The invention discloses a genetic algorithm based minimum-cost optical multicast tree routing method. The method includes two parts of edge initialization of a network and minimum-cost multicast tree iteration. The edge initialization of the network mainly includes completing initialization of edges in the network, representing the edges with integer multiples of unit capacity with multiple unit capacity edges, and conveniently applying a genetic algorithm to optimize an information transmission path and an encoding method. The minimum-cost optical multicast tree iteration mainly includes the steps of selection, intersection, variation, bad gene removal and the like. Some bad genes are removed from a gene pool according to a designed fitness function value in the process of iteration every time, so that size of an algorithm searching space can be greatly decreased, convergence rate of the algorithm is increased beneficially, and an optical multicast tree with less cost is found out. The information transmission routing method is smallest in total number of found and needed information transmission links meeting the requirement on multicast request rate, and minimum in encoding operation times.

Description

technical field [0001] The invention relates to the field of communication technology, in particular to a genetic algorithm-based optical multicast tree minimum cost routing method. Background technique [0002] With the rapid development of multicast services such as video conferencing, Internet TV, and multimedia distance education, the consumption of network bandwidth and the rapid increase of congestion, traditional communication networks are facing insufficient bandwidth resources, low network throughput, and rising service blocking rates. Serious problem, network resources are becoming increasingly strained. Due to the high-bandwidth and high-speed information transmission capabilities of optical networks, it temporarily solves the problem of insufficient bandwidth resources in traditional electric domain networks. However, with the sustained and rapid growth of broadband services and multicast applications in recent years, optical networks are once again facing many ...

AI Technical Summary

Problems solved by technology

However, the existing heuristic algorithms are generally effective in specific networks, but not ideal in other networks, and intelligent optimization algorithms will be better than heuristic algorithms in solving NP-complete problems. It is a relatively mature algorithm among intelligent optimization algorithms.

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