.png){kind=icon}
返回
Multiselection-Based Differential Evolution
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2024.3447051.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Differential evolution (DE) has been developed as a state-of-the-art population-based stochastic optimizer for continuous nonconvex search space in recent decades. It uses difference vectors among individuals to adaptively control the balance between exploration and exploitation for achieving an excellent search performance. Specifically, one-to-one selection in DE guarantees that difference vectors vary from large for exploration to small for exploitation. However, the selection may generate inappropriate scale of difference vectors, which causes insufficient exploitation when facing complex problems with, e.g., multimodal with a huge number of local optima or plain regions. In this article, we propose a multiselection-based DE (MSDE) to address such limitation. Specifically, three different types of selection strategies are adopted to deal with the different situation during a search process. The proposed MSDE is evaluated on the 2013, 2014, and 2017 IEEE congress on evolutionary computation real parameter optimization competitions. Experimental results demonstrate that the proposed MSDE significantly outperforms the winners of those competitions. We further show that, when integrating the proposed multiselection-based strategy with the original DE or other advanced DE variants, it can also improve their search performance on most of test cases. This work makes a significant contribution to advance the state of the art in the area of population-based stochastic optimizers.
Keyword:
Vectors
Optimization
Next generation networking
Convergence
Evolutionary computation
Search problems
Indexes
Continuous optimization
differential evolution (DE)
selection
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
Differential Sensitivity to LPS-Induced Myocardial Dysfunction in the Isolated Brown Norway and DAHL S Rat Hearts.png)
Shock
OAAI
ShockIF0
OAAI分享收藏A Modified Differential Evolution With Distance-based Selection for Continuous Optimization in Presence of NoiseIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Accelerating differential evolution based on a subset-to-subset survivor selection operatorSOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏Using spatial neighborhoods for parameter adaptation: An improved success history based differential evolution使用空间邻域进行参数自适应: 基于成功历史的改进差分进化OAAI
OAAI分享收藏Pre-DEMO: Preference-Inspired Differential Evolution for Multi/Many-Objective Optimization预演示: 用于多/多目标优化的受偏好启发的差分进化
PREAI
分享收藏Differential evolution with orthogonal array -based initialization and a novel selection strategy
PREAI
分享收藏Differential evolution algorithm with fitness and diversity ranking-based mutation operator基于适应度和多样性排序的变异算子差分进化算法
PREAI
分享收藏
查看更多内容