.png){kind=icon}
返回
Evolutionary Dynamic Multiobjective Optimization: Benchmarks and Algorithm Comparisons
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2015.2510698.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Dynamic multiobjective optimization (DMO) has received growing research interest in recent years since many real-world optimization problems appear to not only have multiple objectives that conflict with each other but also change over time. The time-varying characteristics of these DMO problems (DMOPs) pose new challenges to evolutionary algorithms. Considering the importance of a representative and diverse set of benchmark functions for DMO, in this paper, we propose a new benchmark generator that is able to tune a number of challenging characteristics, including mixed Pareto-optimal front (convexity-concavity), nonmonotonic and time-varying variable-linkages, mixed types of changes, and randomness in type change, which have rarely or not been considered or tested in the literature. A test suite of ten instances with different dynamic features is produced from the generator in this paper. Additionally, a few new performance measures are proposed to evaluate algorithms for DMOPs with different characteristics. Six representative multiobjective evolutionary algorithms from the literature are investigated based on the proposed DMO test suite and performance measures. The experimental results facilitate a better understanding of strengths and weaknesses of these compared algorithms for DMOPs.
Keyword:
Benchmark
dynamic multiobjective optimization (DMO)
evolutionary algorithm
performance metric
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:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts一种改进的基于复杂帕累托前沿分解的多目标优化进化算法
OAAI
OAAI分享收藏Refining Estimates of Bird Collision and Electrocution Mortality at Power Lines in the United States.png)
PLoS ONEOAAI
PLoS ONEIF0
OAAI分享收藏A novel cooperative coevolutionary dynamic multi-objective optimization algorithm using a new predictive modelSOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏分享收藏Artificial immune system in dynamic environments solving time-varying non-linear constrained multi-objective problemsSOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏分享收藏分享收藏
查看更多内容