认知智能电网中改进樽海鞘群算法的资源分配算法

申红婷

申红婷.认知智能电网中改进樽海鞘群算法的资源分配算法[J].电测与仪表,2025,62(6):111-119.
SHEN Hong-ting.A resource allocation algorithm based on improved salp swarm algorithm in cognitive smart grid[J].Electrical Measurement & Instrumentation,2025,62(6):111-119.

认知智能电网中改进樽海鞘群算法的资源分配算法

A resource allocation algorithm based on improved salp swarm algorithm in cognitive smart grid

DOI：10.19753/j.issn1001-1390.2025.06.011

中文关键词: 认知智能电网樽海鞘群算法 Holton序列黎曼流形全局搜索

英文关键词:cognitive smart grid, salp swarm algorithm, Holton sequence, Riemannian flow learn, global search

基金项目:贵州省科学基金资助项目(黔科合基础[2020]1Y142)；教育部人文社科研究规划基金项目（22YJAZH009）

作者中文名	作者英文名	单位
申红婷	SHEN Hong-ting	贵州财经大学信息学院

摘要点击次数: 24

中文摘要:

针对智能电网中的频谱资源稀缺问题，利用认知无线电与智能电网融合技术提升智能电网中的频谱资源利用率。对于认知智能电网中最优化资源分配问题的求解，设计了一种混沌群精英领导者与黎曼流形的樽海鞘群算法。文章利用Halton序列混沌初始化樽海鞘种群，增强多样性，使算法快速锁定最优解范围；为避免领导者陷入局部最优，利用群体精英随机替换领导者位置更新，从而避免领导者陷入局部最优，提升领导者搜索能力。另外，提出融合黎曼流形和学生t分布变异策略，增强种群活跃度，克服算法在后期因群体聚集导致算法陷入局部最优缺陷。利用IEEE CEC基准函数集测试改进算法的有效性，并绘制曲线进行有效性分析；为将改进的混沌自适应樽海鞘群算法(modified chaotic adaptive salp swarm algorithm,MCASSA)应用到认知智能电网中的资源分配求解的应用潜力，以智能电网的最大化传输速率为目标进行对比分析，并比较分析用户的公平性和最大化效益。实验结果表明，MCASSA算法能有效提升认知智能电网的性能和资源利用率。

英文摘要:

Aiming at the scarcity of spectrum resources in smart grid, the integration technology of cognitive radio and smart grid is used to improve the utilization of spectrum resources in smart grid. To solve the problem of optimal resource allocation in cognitive smart grid, a chaotic swarm elite leader and Riemannian manifold salp swarm algorithm is designed. Halon sequence chaos is used to initialize the salp population, which enhances the diversity and enables the algorithm to quickly lock the optimal solution range. In order to avoid leaders falling into local optimization, swarm elites are used to randomly replace the position updates of leaders, so as to avoid leaders falling into local optimization and improve the search ability of leaders. In addition, a mutation strategy combining Riemannian manifold and student t-distribution is proposed to enhance the activity of the population and overcome the defect that the algorithm falls into local optimization due to population aggregation in the later stage. The IEEE CEC benchmark function set is used to test the effectiveness of the improved algorithm, and the curves are drawn for effectiveness analysis. In order to apply modified chaotic adaptive salp swarm algorithm(MCASSA) algorithm to the application potential of resource allocation solution in cognitive smart grid, a comparative analysis is carried out with the goal of maximizing the transmission rate of smart grid, and the fairness and maximum benefit of users are compared and analyzed. Experimental results show that MCASSA algorithm can effectively improve the performance and resource utilization of cognitive smart grid.

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