Multi-objective particle swarm optimization algorithm based on dynamic crowding distance and its application

被引：0

作者：

Liu L. ^{[1
,2
]}

Zhang X. ^{[2
]}

Xie L. ^{[1
]}

Li M. ^{[2
]}

Wen S. ^{[2
]}

Lu Q. ^{[2
]}

机构：

[1] College of Mechanical Electronic Engineering, Lanzhou University of Technology

[2] College of Mechanical Electronic Engineering, Taiyuan University of Science and Technology

来源：

Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery | 2010年 / 41卷 / 03期

关键词：

Dynamic crowding distance; Improved quick sorting; Multi-objective optimization; Pareto set; Particle swarm algorithm;

D O I：

10.3969/j.issn.1000-1298.2010.03.039

中图分类号：

学科分类号：

摘要：

A multi-objective particle swarm optimization algorithm based on dynamic crowding distance (DCD-MOPSO) was proposed. Applying the improved quick sorting to reduce the time for computation, both the dynamic inertia weight and acceleration coefficients were used in the algorithm to explore the search space more efficiently. A new diversity strategy called dynamic crowding distance was used to ensure sufficient diversity amongst the solutions of the non-dominated fronts. Some benchmark functions and the optimization of four-bar plane truss were tested to compare with the performance of DCD-MOPSO and NSGAII. The results show that DCD-MOPSO has better convergence with even distributing of Pareto set.

引用

页码：189 / 194

页数：5

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