基于双种群粒子群优化新算法的最优潮流求解

提出一种带赌轮选择的双种群粒子群优化算法(TSPSO)求解最优潮流问题。在该算法中,对2个种群采取不同的参数设置,使得粒子在进化过程中具有不同的飞行轨迹,从而尽可能地探索解空间,增强算法的全局搜索能力;基于赌轮算法的概率选择机制使粒子可以在较好的可行解邻近范围内高强度搜索,增强了算法的局部搜索能力;采用自适应惩罚因子能有效区分最优潮流的目标函数和约束条件对种群进化的影响,使种群可以跨越不可行域到可行域进行搜索。通过IEEE30节点系统对该算法进行测试,结果表明,采用该算法可以有效求解最优潮流问题。

A novel two-swarm based particle swarm optimization algorithm for optimal power flow problem

A novel two-swarm based PSO algorithm (TSPSO) with roulette wheel selection was proposed to solve optimal power flow problem.With different parameter settings,the two swarms have different flying trajectory,explore solution space as much as possible,and enhance the global exploration ability.Roulette-wheel-selection based on stochastic selection scheme makes particles search in the neighborhood of better feasible solution intensive and enhances the local exploitation ability.Adaptive penalty coefficients can effectively balance objective function and constraints in the process of swarm evolution and make particles search from infeasible region to feasible region.The proposed algorithm was tested on IEEE30 bus system and the results show that it can effectively be solved optimal power problem.