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针对引力搜索算法(GSA)初始群体随机和易陷入局部最优的缺点,提出了一种改进引力搜索算法(CLGSA),该算法引入混沌运动优化初始群体,提高种群的多样性,并结合Levy飞行的随机游走策略更新粒子的位置,降低算法陷入局部最优的可能。将GSA和CLGSA对IEEE14节点测试系统进行电力系统无功优化仿真。仿真结果表明,CLGSA不仅具有良好的优化效果,且能有效降低系统的有功功率损耗。
In order to overcome the shortcomings of initial population of gravitational search algorithm (GSA) randomly and easily falling into local optimum, an improved gravitation search algorithm (CLGSA) is proposed. This algorithm introduces the chaos optimization initial population to improve the population diversity, The random walk strategy updates the position of the particle and reduces the possibility of the algorithm getting into the local optimum. GSA and CLGSA IEEE14 node test system for power system reactive power optimization simulation. The simulation results show that CLGSA not only has a good optimization effect, but also can effectively reduce the active power loss of the system.