【摘要】 粒子群优化算法是通过粒子记忆、追随当前最优粒子,并不断更新自己的位置和速度来寻找问题的最优解。为了克服标准粒子群算法存在着早熟收敛、难以处理问题约束条件等缺点,本研究对递减惯性权值进行了改进,将其表示为粒子群进化速度与群体平均适应度方差的函数;给出了适合PSO算法的约束处理机制,提出了一种改进自适应粒子群算法,并将其应用于水库优化调度中。实例计算并与经典方法相比,表明该方法原理简单、易编程实现,能以较快的速度收敛于全局最优解。更多还原

【Abstract】 Particle swarm optimizer(PSO) searches the best solution of a problem by remembering and following the excellent particle and updating own position and speed continuously.In order to overcome the defect of premature and difficulty of dealing with constraint,this paper presents a modified adaptive PSO(MAPSO) which expresses inertia weight in a function determined by the evolution speed and the fitness variance of particle swarms and proposes a constraint handling strategy suit for PSO.A hydropower station operation demonstrates the successful application of the modified adaptive PSO.Study results show that the MAPSO is a simple,programming easily optimal algorithm and can find the global optimum solution quickly compared with traditional method.更多还原