.png){kind=icon}
返回
A multi-resolution grid-based bacterial foraging optimization algorithm for multi-objective optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2022.101098.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In recent years, bacterial foraging optimization (BFO) has been used to solve multiobjective optimization problems (MOPs). However, BFO has not fully developed its potentials on MOPs for the reason of lacking of in-depth research on the optimization mechanisms and the diversity maintenance strategies. To solve it, this paper develops a multi-resolution grid-based BFO algorithm (called as MRBFO). MRBFO redesigns four tailored optimization mechanisms for MOPs including chemotaxis, conjugation, reproduction, and elimination and dispersal to search optimal nondominated solutions. Moreover, MRBFO defines a multi-resolution grid strategy to produce well-distributed diverse nondominated solutions. The performance of MRBFO is comprehensively evaluated by comparing it with several state-of-the-art algorithms on many benchmark test problems. The empirical results have sufficiently verified the advantages of MRBFO.
Keyword:
Multiobjective optimization problems
Bacterial foraging optimization
Diversity maintenance
Multi-resolution grid
期刊
IF:
8.5
论文数:
2.2K
被引数:
1.0W
机构
引用论文
A Multistage Evolutionary Algorithm for Better Diversity Preservation in Multiobjective Optimization
PREAI
分享收藏Ground water sensitivity to climate variability in the white Bandama basin, Ivory Coast.png)
SpringerPlus
OAAI
SpringerPlusIF0
OAAI分享收藏PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective OptimizationPlatEMO: 用于进化多目标优化的MATLAB平台
OAAI
OAAI分享收藏分享收藏
查看更多内容