In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm (ACOA) was used to optimize the reactive power compensation for the rural distribution system. In this stud...In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm (ACOA) was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects: one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county's distribution network showed that the improved ACOA was practicable.展开更多
武器-目标分配(weapon target assignment,WTA)问题是根据武器对来袭目标毁伤概率的不同,合理确定待打击目标的武器分配方案,以达到用尽可能少的武器对来袭目标毁伤程度最大化的目的,是作战指挥决策领域的重要研究内容。在构建WTA问题...武器-目标分配(weapon target assignment,WTA)问题是根据武器对来袭目标毁伤概率的不同,合理确定待打击目标的武器分配方案,以达到用尽可能少的武器对来袭目标毁伤程度最大化的目的,是作战指挥决策领域的重要研究内容。在构建WTA问题模型的基础上,针对传统灰狼优化(grey wolf optimization,GWO)算法局部开发能力不足等问题,采取了一种精英保留及免疫变异局部搜索策略。改进灰狼优化算法(improved grey wolf optimization,IGWO)首先在灰狼种群中选择部分优质精英个体,然后通过随机点变异和受体编辑两种免疫局部搜索策略对精英个体进一步寻优,从而改善传统GWO算法过早收敛和易陷入局部最优的缺点。针对3种不同规模的武器-目标分配问题,将IGWO与交叉熵算法、传统GWO算法进行了对比,计算结果显示IGWO算法所求适应度值的分位数均明显高于对比算法,进而验证了IGWO算法的有效性。展开更多
基金Supported by China Postdoctoral Science Foundation(20090460873)
文摘In view of the serious reactive power loss in the rural network, improved ant colony optimization algorithm (ACOA) was used to optimize the reactive power compensation for the rural distribution system. In this study, the traditional ACOA was improved in two aspects: one was the local search strategy, and the other was pheromone mutation and re-initialization strategies. The reactive power optimization for a county's distribution network showed that the improved ACOA was practicable.
文摘武器-目标分配(weapon target assignment,WTA)问题是根据武器对来袭目标毁伤概率的不同,合理确定待打击目标的武器分配方案,以达到用尽可能少的武器对来袭目标毁伤程度最大化的目的,是作战指挥决策领域的重要研究内容。在构建WTA问题模型的基础上,针对传统灰狼优化(grey wolf optimization,GWO)算法局部开发能力不足等问题,采取了一种精英保留及免疫变异局部搜索策略。改进灰狼优化算法(improved grey wolf optimization,IGWO)首先在灰狼种群中选择部分优质精英个体,然后通过随机点变异和受体编辑两种免疫局部搜索策略对精英个体进一步寻优,从而改善传统GWO算法过早收敛和易陷入局部最优的缺点。针对3种不同规模的武器-目标分配问题,将IGWO与交叉熵算法、传统GWO算法进行了对比,计算结果显示IGWO算法所求适应度值的分位数均明显高于对比算法,进而验证了IGWO算法的有效性。