.png){kind=icon}
返回
Dynamic multi-objective evolutionary algorithms for single-objective optimization
.png){kind=icon}
OA
AI
DOI：10.1016/j.asoc.2017.08.030.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes a new method for handling the difficulty of multi-modality for the single-objective optimization problem (SOP). The method converts a SOP to an equivalent dynamic multi-objective optimization problem (DMOP). A new dynamic multi-objective evolutionary algorithm (DMOEA) is implemented to solve the DMOP. The DMOP has two objectives: the original objective and a niche-count objective. The second objective aims to maintain the population diversity for handling the multi-modality difficulty during the search process. Experimental results show that the performance of the proposed algorithm is significantly better than the state-of-the-art competitors on a set of benchmark problems and real world antenna array problems. (C) 2017 Elsevier B.V. All rights reserved.
Keyword:
Evolutionary computation
Multi-objective optimization
Niching
Dynamic optimization
Antenna array
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
.png){kind=icon}
Performance of Laplacian Biogeography-Based Optimization Algorithm on CEC 2014 continuous optimization benchmarks and camera calibration problem基于拉普拉斯生物地理学的优化算法在CEC上的性能2014连续优化基准和摄像机标定问题
PREAI
分享收藏分享收藏Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization求解全局数值优化问题的策略自适应差分进化算法
OAAI
OAAI分享收藏Effects of two cooling garments on post-exercise thermal comfort of female subjects in the heat
PREAI
分享收藏分享收藏分享收藏Seeking Multiple Solutions: An Updated Survey on Niching Methods and Their Applications寻求多种解决方案: 关于小众方法及其应用的最新调查OAAI
OAAI分享收藏
查看更多内容