Multicast Routing Calculation Under Multiple QoS Constraints in Communication Networks
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Resource Overview
This paper presents an enhanced genetic algorithm named HGA-QoSR for multicast routing computation under multiple QoS constraints in communication networks. By integrating simulated annealing's local optimization capability with genetic algorithm's global search ability, and employing an isolated niche mechanism to control population evolution, the algorithm maintains ecological diversity during evolution. This approach improves computational efficiency and solution quality. Theoretical analysis and simulation experiments demonstrate significant performance improvements compared to traditional genetic algorithms, with implementation featuring adaptive mutation rates and fitness-based selection mechanisms.
Detailed Documentation
We propose an improved genetic algorithm HGA-QoSR for multicast routing calculation under multiple QoS constraints in communication networks, based on simulated annealing technology. The algorithm organically combines the local optimization capability of simulated annealing with the global search ability of genetic algorithms, while employing an isolated niche mechanism to control independent population evolution. This design maintains ecological diversity during the evolutionary process, thereby enhancing algorithm efficiency and solution quality. The implementation includes key components such as: 1) Temperature-controlled acceptance probability for solution transitions, 2) Niche-based fitness sharing to preserve population diversity, and 3) Elite preservation strategy to maintain optimal solutions. Theoretical analysis and simulation experiments confirm that our improved algorithm shows significant performance enhancements compared to traditional genetic algorithms, particularly in handling complex QoS constraints like bandwidth, delay, and packet loss requirements.
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