N-体仿真中的分层树形算法

考虑两种情况 :在 3维空间中给出n个质点 ,计算每一粒子施加在其它粒子上的力 ,成对的相互作用可能有万有引力或者Lennard_Jones.上述两种情况的力 ,当两粒子间的距离达到无限大时消失 .既然n个质点 ,两两相互作用共有 [n(n - 1) ]/ 2对 ,直接算法对力的估算所需时间为O(n2 ) .这对天文中的仿真所用时间是非常大的 .该文提出了一种O(logn)算法 ,使用n/logn处理器CREWPRAM来计算n体仿真中的场 .这种最优并行算法的关键是利用一个相同的非递归自上而下的过程来代替一个递归的自上而下的计算过程 .这种相似的算法对力场计算也产生了一个新的O(n)时间序列算法 .

Generation Reliability Assessment in Electricity Markets Using Cooperative and Non-cooperative Concepts and Intelligent Systems

Deregulation policy has caused some changes in the concepts of power systems reliability assessment and enhancement. In the present research, generation reliability is considered, and a method for its assessment is proposed using game theory and NN （neural networks）. Also, due to the stochastic behavior of power markets and generators＇ forced outages, MCS （monte carlo simulation） is used for reliability evaluation. Generation reliability merely focuses on the interaction between generation complex and load. Therefore, in the research, based on the behavior of players in the market and using game theory, two outcomes are considered： cooperation and non-cooperation. The proposed method is assessed on IEEE-Reliability Test System with satisfactory results. LOLE （loss of load expectation） is used as the reliability index and it will be shown that generation reliability in cooperation market is better than non-cooperation outcome.