基于禁忌搜索粒子群优化算法的无功优化

针对粒子群算法局部搜索能力较弱和存在早熟收敛的问题,提出将粒子群优化算法结合禁忌搜索的混合算法,并应用它来求解电力系统无功优化问题。该混合算法是以粒子群优化算法为主框架,以禁忌搜索算法作为个体群继续在邻域中寻优,寻优结果对粒子群算法的输出做了更新。混合算法保留了粒子群优化算法的并行处理性,同时利用了禁忌搜索算法的较强的"爬山"能力,加快了混合优化算法的收敛时间和提高了收敛解的有效性。

Reactive Power Optimization Based on Particle Swarm Optimization Algorithm with Tabu search

Weaker local search and premature convergence are two key problems existing in the particle swam optimization algorithm(PSO).To overcome the shortcomings,a hybrid algorithm of particle swam optimization algorithm(PSO)and tabu search algorithm(TS)is used to get the solution of reactive power optimization.It adopts a main framework which is particle swam optimization algorithm,and individuals seeks optimization in the neighboring regions with tabu search and updates the result of seeking optimization for particle swam optimization algorithm.It not only retains parallel processing of PSO algorithm,but also adopts local hill-climbing of TS algorithm,thus quickening convergent time and improving effectively optimum solution.