文件名称:duoboluyou
- 所属分类：
- 人工智能/神经网络/遗传算法
- 资源属性：
- [Matlab] [源码]
- 上传时间：
- 2012-11-26
- 文件大小：
- 27kb
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- 0次
- 提 供 者：
- S5145*****
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针对通信网络中多重QoS约束条件下的多播路由计算，提出了一个基于模拟退火技术的改进遗传算法HGA-QoSR。该算法把模拟退火技术的局部寻优能力与遗传算法的全局寻优能力有机结合，并利用隔离小生境机制控制种群的独立进化，使演化过程中的种群保持生态多样性，以提高算法运行效率和解的质量。理论分析和仿真实验表明，与传统遗传算法相比较，该算法性能有显著改进。 -Communication networks for multi-QoS Constrained multicast routing calculation, proposed a simulated annealing technique based on improved genetic algorithm HGA-QoSR. The algorithm to simulated annealing and local optimization ability of genetic algorithms global optimization ability of organic combination and isolation niche mechanism to control the use of independent evolution of populations, the evolution of the species to maintain ecological diversity, to improve the algorithm efficiency settlement quality. Theoretical analysis and simulation results show that compared with traditional genetic algorithm, the algorithm performance significantly improved.
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下载文件列表
多播路由小生境遗传算法\adding_point_adjacency.asv
......................\adding_point_adjacency.m
......................\average_fitness_computing.m
......................\brocast_best.asv
......................\brocast_best.m
......................\check_network_points.asv
......................\check_network_points.m
......................\chromosome_mutation.asv
......................\chromosome_mutation.m
......................\clear_same_pop.asv
......................\clear_same_pop.m
......................\clear_worst_pop.asv
......................\clear_worst_pop.m
......................\crossover.asv
......................\crossover.m
......................\direct_adjacency.asv
......................\direct_adjacency.m
......................\distance_computing.m
......................\evaluate_chromosome.asv
......................\evaluate_chromosome.m
......................\evaluate_pop.m
......................\evaluate_pops.m
......................\find_best.asv
......................\find_best.m
......................\find_worst.m
......................\generate_population.m
......................\get_destination_nodes.asv
......................\get_destination_nodes.m
......................\is_same_pop.asv
......................\is_same_pop.m
......................\is_valid_path.m
......................\make_adjacency_matrix.m
......................\make_adjacency_matrix_by_two_chromosomes.m
......................\make_new_chromosome.asv
......................\make_new_chromosome.m
......................\mc_nga_main.asv
......................\mc_nga_main.m
......................\multicast_routing_problem.asv
......................\multicast_routing_problem.m
......................\mutation.asv
......................\mutation.m
......................\mutation_new_path.asv
......................\mutation_new_path.m
......................\replace_pop.m
......................\selection.m
......................\Untitled4.m
多播路由小生境遗传算法
......................\adding_point_adjacency.m
......................\average_fitness_computing.m
......................\brocast_best.asv
......................\brocast_best.m
......................\check_network_points.asv
......................\check_network_points.m
......................\chromosome_mutation.asv
......................\chromosome_mutation.m
......................\clear_same_pop.asv
......................\clear_same_pop.m
......................\clear_worst_pop.asv
......................\clear_worst_pop.m
......................\crossover.asv
......................\crossover.m
......................\direct_adjacency.asv
......................\direct_adjacency.m
......................\distance_computing.m
......................\evaluate_chromosome.asv
......................\evaluate_chromosome.m
......................\evaluate_pop.m
......................\evaluate_pops.m
......................\find_best.asv
......................\find_best.m
......................\find_worst.m
......................\generate_population.m
......................\get_destination_nodes.asv
......................\get_destination_nodes.m
......................\is_same_pop.asv
......................\is_same_pop.m
......................\is_valid_path.m
......................\make_adjacency_matrix.m
......................\make_adjacency_matrix_by_two_chromosomes.m
......................\make_new_chromosome.asv
......................\make_new_chromosome.m
......................\mc_nga_main.asv
......................\mc_nga_main.m
......................\multicast_routing_problem.asv
......................\multicast_routing_problem.m
......................\mutation.asv
......................\mutation.m
......................\mutation_new_path.asv
......................\mutation_new_path.m
......................\replace_pop.m
......................\selection.m
......................\Untitled4.m
多播路由小生境遗传算法