.png){kind=icon}
返回
An evolutionary algorithm for dynamic multi-objective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.amc.2008.05.151.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, the dynamic multi-objective optimization problem (DMOP) is first approximated by a series of static multi-objective optimization problems (SMOPs) by dividing the time period into several equal subperiods. In each subperiod, the dynamic multi-objective optimization problem is seen as a static multi-objective optimization problem by taking the time parameter fixed. Then, to decrease the amount of computation and efficiently solve the static problems, each static multi-objective optimization problem is transformed into a two-objective optimization problem based on two re-defined objectives. Finally, a new crossover operator and mutation operator adapting to the environment changing are designed. Based on these techniques, a new evolutionary algorithm is proposed. The simulation results indicate that the proposed algorithm can effectively track the varying Pareto fronts with time. (C) 2008 Published by Elsevier Inc.
Keyword:
Evolutionary algorithm
Dynamic multi-objective programming
Uniform design
期刊
IF:
3.4
论文数:
2.3W
被引数:
3.3W
机构
引用论文
Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
.png){kind=icon}
PREAI
分享收藏分享收藏
没有更多内容
没有更多内容