基于免疫记忆克隆选择算法的多目标无功优化

Multi-objective reactive power optimization based on immune memory colonial selection algorithm

结合免疫记忆学说和克隆选择原理,提出了一种解决多目标无功优化问题的免疫记忆克隆选择算法。该算法针对多目标无功优化问题的特点,采用以拥挤距离为适应度的自适应克隆方式,实现了种群的扩张,保证了所得解集的均匀性;引入非一致性变异算子,使该算法同时具备全局均匀搜索能力和局部精确寻优能力;采用交叉重组算子实现了抗体间的协作,促进不同抗体间信息的交流;通过抗体群更新操作,一方面保证了算法的收敛速度,另一方面确保了所得解集均匀分布;引入记忆单元概念,可以有效抑制寻优过程中出现的退化现象,确保了种群的多样性。以IEEE-14和IEEE-118节点测试系统为例进行仿真计算,结果表明该算法可以有效提高系统运行的安全性和经济性,是求解多目标无功优化问题的有效方法。

Based on immune memory theory and colonial selection principle, a new immune memory colonial selection algorithm is put forward to solve the problem of multi-objective reactive power optimization. Crowded distance is used as fitness in the adaptive cloning operation to realize the population expansion and ensure the uniformity of solution sets. By use of non consistent variation operation, the algorithm has both global well-distributed search ability and local accurate optimization ability. The cross restructuring operator can promote the information communication between different antibodies. It effectively increases the speed of the algorithm and ensures the uniformity of solution sets by use of antibody group update operation. The introduction of memory unit concept has contributed to suppression of degeneration, ensuring the diversity of population. IEEE-14 bus system and 1EEE-118 bus system are used to verify the performance of the proposed algorithm, and the results show that it is an effective method for multi-objective reactive power optimization.