基于改进粒子群算法的机组负荷优化分配

Unit Load Optimal Dispatch Based on Improved Particle Swarm Optimization Algorithm

针对标准粒子群算法存在容易早熟收敛的问题,在分析机组负荷优化问题的基础上,提出了一种基于解约束机制、边界反弹规则、高斯分布序列和混沌序列的改进粒子群算法。算法采用解约束机制和边界反弹规则处理优化问题的约束条件,同时在粒子移动过程中引入了高斯分布序列和混沌序列,从而克服了算法过早收敛的缺陷,提高了算法的全局优化能力。实例计算结果表明,该算法具有稳定的全局优化能力,为机组负荷优化分配问题的求解提供了新的方法。

To overcome the easy convergence of standard particle swarm optimization algorithm, an improved particle swarm optimization based on unconstraint mechanism, border rebound rule, gaussian distribution sequences and chaotic sequence is proposed by the analysis of unit load optimal dispatch. The algorithm use uneonstraint mechanism and border rebound rule to release the constraints, and introduce gaussian distribution sequences and chaotic sequence into the move process of particle. By the modification mentioned above, the optimal ability of the algorithm is improved. The simulation result show the proposed algorithm has the stable global optimization capability and provide a new approach for the unit load optimal dispatch.