基于经济负荷分配的混沌迭代粒子群算法

Chaotic iteration particle swarm optimization algorithm based on economic load dispatch

为高效求解复杂的非凸、非线性电力系统经济负荷分配问题,提出了一种混沌迭代粒子群算法:粒子群算法的全局搜索能力很强,但易陷入局部最优,混沌的遍历性特性可有效抑制早熟现象。将最优迭代因子引入粒子群算法,对经粒子群算法搜索后的先验解进行基于一种新Tent映射的混沌变异,并改进算法的迭代策略,以平衡粒子的全局和局部性搜索,避免了早熟收敛。通过6机组、15机组的仿真试验,以及同其他算法仿真结果的比较,验证了本算法良好的收敛性和寻优性。

To solve the non-convex and non-linear economic dispatch problem efficiently,a chaotic iteration particle swarm optimization algorithm is presented.In the global research of particle swarm optimization and lo-cal optimum,ergodicity of chaos can effectively restrain premature.To balance the exploration and exploitation abilities and avoid being trapped into local optimal,a new index,called iteration best,is incorporated into particle swarm optimization,and chaotic mutation with a new Tent map imported can make local search within the prior knowledge,a new strategy is proposed in iteration strategy.The algorithm is validated for two test sys-tems consisting of 6 and 15 generators.Compared with other methods in this literature,the experimental resul demonstrates the high convergency and effectiveness of proposed algorithm.