Transformations of Steiner tree problem variants have been frequently discussed in the literature. Besides allowing to easily transfer complexity results, they constitute a central pillar of exact state-of-the-art sol...Transformations of Steiner tree problem variants have been frequently discussed in the literature. Besides allowing to easily transfer complexity results, they constitute a central pillar of exact state-of-the-art solvers for well-known variants such as the Steiner tree problem in graphs. In this article transformations for both the prize-collecting Steiner tree problem and the maximum-weight connected subgraph problem to the Steiner arborescence problem are introduced for the first time. Furthermore, the considerable implications for practical solving approaches will be demonstrated, including the computation of strong upper and lower bounds.展开更多
针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法在局部阴影条件下易陷入局部最优以及粒子群优化算法(Particle Swarm Optimization,PSO)存在的收敛速度慢和易陷入局部最优等问题,提出一种基于自适应粒子群优化(Adapt...针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法在局部阴影条件下易陷入局部最优以及粒子群优化算法(Particle Swarm Optimization,PSO)存在的收敛速度慢和易陷入局部最优等问题,提出一种基于自适应粒子群优化(Adaptive Particle Swarm Optimization,APSO)算法的复合MPPT方法。在APSO中,初始化阶段使用拉丁超立方抽样代替随机初始化,使用自适应惯性权重策略来平衡算法的探索和开发,在提高算法收敛速度的同时,避免陷入局部最优解。通过仿真实验证明该算法在局部阴影下能够跳出局部最优,快速收敛到最大功率点处,与LPSO、PSO算法进行对比,所提算法具有更快的追踪速度、更高的追踪效率和更强的鲁棒性。展开更多
文摘Transformations of Steiner tree problem variants have been frequently discussed in the literature. Besides allowing to easily transfer complexity results, they constitute a central pillar of exact state-of-the-art solvers for well-known variants such as the Steiner tree problem in graphs. In this article transformations for both the prize-collecting Steiner tree problem and the maximum-weight connected subgraph problem to the Steiner arborescence problem are introduced for the first time. Furthermore, the considerable implications for practical solving approaches will be demonstrated, including the computation of strong upper and lower bounds.
文摘针对传统的最大功率点追踪(Maximum Power Point Tracking,MPPT)算法在局部阴影条件下易陷入局部最优以及粒子群优化算法(Particle Swarm Optimization,PSO)存在的收敛速度慢和易陷入局部最优等问题,提出一种基于自适应粒子群优化(Adaptive Particle Swarm Optimization,APSO)算法的复合MPPT方法。在APSO中,初始化阶段使用拉丁超立方抽样代替随机初始化,使用自适应惯性权重策略来平衡算法的探索和开发,在提高算法收敛速度的同时,避免陷入局部最优解。通过仿真实验证明该算法在局部阴影下能够跳出局部最优,快速收敛到最大功率点处,与LPSO、PSO算法进行对比,所提算法具有更快的追踪速度、更高的追踪效率和更强的鲁棒性。
