粒子群和变邻域差分进化搜索算法在多目标无功优化中的应用

目前很少研究多目标无功优化问题,而应用到多目标无功优化的多数智能算法容易陷入局部最优点.提出采用变邻域差分进化搜索与BPSO混合算法对网络无功优化,该算法具有并行处理特点、参数少容易控制、收敛速度快,很适合处理多目标无功优化问题,该算法不仅能够保证群体的多样性并且又能继承上一代的优越性,达到了多目标的要求,通过算例验证...

Application of Particle Swarm and Variable Neighborhood Differiential Evolution Search Algorithm in Multi-objective Reactive Power Optimization

Few of multi-objective reactive power optimization problems are studied, the majority of intelligent algorithm applied with multi-objective reactive power optimization is easy to fall into local optima. This paper proposes differential evolution with variable neighborhood search and BPSO hybrid algorithm for network reactive power optimization, the algorithm with the features of parallel processing, less parameters and easy to control, fast convergence, it is suitable for handling multi-objective reactive power optimization problem, the algorithm can guarantee the diversity of swarm and succession of superiority of previous generation, satisfied multi-objective requirement, through given numerical example validates that this algorithm can converge to global optimal solution with feasibility and rationality.