.png){kind=icon}
返回
A Geometric Structure-Based Particle Swarm Optimization Algorithm for Multiobjective Problems
.png){kind=icon}
OA
AI
DOI：10.1109/TSMC.2016.2523938.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a novel evolutionary strategy for multiobjective optimization in which a population's evolution is guided by exploiting the geometric structure of its Pareto front. Specifically, the Pareto front of a particle population is regarded as a set of scattered points on which interpolation is performed using a geometric curve/surface model to construct a geometric parameter space. On this basis, the normal direction of this space can be obtained and the solutions located exactly in this direction are chosen as the guiding points. Then, the dominated solutions are processed by using a local optimization technique with the help of these guiding points. Particle populations can thus evolve toward optimal solutions with the guidance of such a geometric structure. The strategy is employed to develop a fast and robust algorithm based on correlation analysis for solving the optimization problems with more than three objectives. A number of computational experiments have been conducted to compare the algorithm to another three popular multiobjective algorithms. As demonstrated in the experiments, the proposed algorithm achieves remarkable performance in terms of the solutions obtained, robustness, and speed of convergence.
Keyword:
Evolution strategy
geometric structure
multi-objective optimization
nonuniform rational B-spline
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
.png){kind=icon}
A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏分享收藏
查看更多内容