多目标无功优化的向量评价自适应粒子群算法

为了克服粒子群算法在高维复杂问题寻优时有相当可能陷入局部极优的现象，提出了一种自适应粒子群算法。该算法利用种群多样性信息对惯性权重进行非线性的调整，并在算法的后期引入速度变异算子和位置交叉算子，使算法摆脱后期易于陷入局部最优点的束缚。对基于向量评价的粒子群算法进行了扩展，提出了基于向量评价的自适应粒子群算法(vector evaluated particle adaptive swarm optimization，VEAPSO)来解决多目标无功优化问题，求解出问题的Pareto最优解集。为帮助决策者从Pareto最优解集中选取合适的最优解，该文提出一种基于决策者偏好及投影寻踪模型的多属性决策法，使决策结果更加真实可靠。将该算法应用于多目标无功优化问题中，IEEE 30和IEEE 118节点系统算例仿真表明该方法用于解决多目标无功优化问题是有效可行的。

Vector Evaluated Adaptive Particle Swarm Optimization Algorithm for Multi-objective Reactive Power Optimization

An adaptive particle swarm optimization algorithm (APSO) was presented to solve the problem that the conventional PSO algorithm was easy to fall into a locally optimized point. In this algorithm, inertia weight was nonlinearly adjusted by using population diversity information. Velocity mutation factor and position interchange factor were both introduced and the global performance was clearly improved. The vector evaluated particle adaptive swarm optimization algorithm (VEAPSO) was proposed to solve the multi-objective optimization problems. The algorithm had been applied to multi-objective reactive power optimization and can obtain the Pareto optimal solutions. Aiming at defect in the traditional evaluation of multi-objective solutions, a multiple attribute decision-making method based on preference information and projecting pursuit classification model was presented. This method made decision-making result more actual. The algorithm had been applied to multi-objective reactive power optimization studies. The simulation results of the standard IEEE-30-bus and IEEE-118-bus power system had indicated that it was validity and with higher computation efficiency during the multi-objective reactive power optimization.