A Multi-Objective Evolutionary Algorithm Based on Adaptive Grid

被引：0
|
作者：
Yu, Bonan [1 ]
Gu, Tianlong [2 ]
Chang, Liang [2 ]
Li, Li [3 ]
Lan, Rushi [3 ]
Sun, Peng [1 ]
机构：
[1] Guilin Univ Elect Technol, Sch Elect Engn & Automat, Guilin, Peoples R China
[2] Satellite Nav Positioning & Locat Serv, Natl Local Joint Engn Res Ctr, Guilin, Peoples R China
[3] Guilin Univ Elect Technol, Guilin, Peoples R China
来源：
2019 9TH INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND TECHNOLOGY (ICIST2019) | 2019年
关键词：
multi-objective optimization evolutionary algorithm; adaptive grid; Pareto solution;
D O I：
10.1109/icist.2019.8836928
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
With the wide application of optimization problems in engineering practice and scientific research, evolutionary algorithms have been gradually studied. Recently, the main solutions for multi-objective optimization problems include multi-objective evolutionary algorithm based on Pareto dominance or decomposition, and hybrid algorithm. Ground on this, we propose the IGNSGA algorithm, which combines the NSGA-II algorithm based on the Pareto dominance relationship with the optimization mechanism of adaptive meshing after adding the dominant individual accumulation strategy. This way can increase the search pressure, improve the search efficiency, and guarantee the convergence and distribution of Pareto solution, which makes the algorithm have universal applicability.
引用
收藏
页码：71 / 77
页数：7
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