Improved adaptive multiobjective particle swarm algorithm

被引：0

作者：

Cao, Yi-Jia ^{[1
]}

Cao, Li-Hua ^{[1
]}

Li, Yong ^{[1
]}

Xin, Jian-Bo ^{[2
]}

机构：

[1] College of Electrical and Information Engineering, Hunan Univ, Changsha,Hunan,410082, China

[2] Jiangxi Electric Power Research Institute, Nanchang,Jiangxi,330096, China

来源：

Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences | 2014年 / 41卷 / 10期

关键词：

Inverse problems - Flexible AC transmission systems - Pareto principle - Particle swarm optimization (PSO) - Constrained optimization;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Boundary handling and global best guider selection operators play an important role in the multiobjective particle swarm optimization (MOPSO) algorithm. Considering the characteristics of different operators, an improved adaptive MOPSO was proposed. When the algorithm falls into a local optimum, enable the crossover and mutation operators; when the convergence of algorithm hasn't improved in a given duration, switch the boundary handling operator between the truncation and the exponential distribution truncation methods; when the diversity of algorithm hasn't improved in a given duration, switch the global best guider selection operator between the probability inverse proportion to the crowding distance and the probability inverse proportion to the number of dominating solutions methods. The results of the benchmark functions and the optimal allocation problem of flexible AC transmission system (FACTS) devices confirm the effectiveness of proposed algorithm.

引用

页码：84 / 90