.png){kind=icon}
返回
Preference-based cone contraction algorithms for interactive evolutionary multiple objective optimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2019.100602.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We introduce a family of interactive evolutionary algorithms for Multiple Objective Optimization (MOO). In the phase of preference elicitation, a Decision Maker (DM) is asked to compare some pairs of solutions from the current population. Such holistic information is represented by a set of compatible instances of achievement scalarizing or quasi-convex preference functions, which contribute to the construction of preference cones in the objective space. These cones are systematically contracted during the evolutionary search, because an incremental specification of the DM's pairwise comparisons is progressively reducing the DM's most preferred region in the objective space. An inclusion of evolved solutions in this region is used along with the dominance relation to revise an elitism principle of the employed optimization algorithm. In this way, the evolutionary search is focused on a subset of Pareto optimal solutions that are particularly interesting to the DM. We investigate, moreover, how the convergence is influenced by the use of some pre-defined and newly proposed self-adjusting (dynamic) interaction patterns. We also propose a new way for visualizing the progress of an evolutionary search. It supports understanding the differences between effects of a selection pressure imposed by various optimization algorithms.
Keyword:
Multiple objective optimization
Interactive evolutionary hybrids
Cone contraction
Pairwise comparisons
Interaction patterns
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:
8.5
论文数:
2.2K
被引数:
1.0W
机构
引用论文
.png){kind=icon}
Using Choquet integral as preference model in interactive evolutionary multiobjective optimization在交互式进化多目标优化中使用Choquet积分作为偏好模型
OAAI
OAAI分享收藏Convex preference cone-based approach for many objective optimization problems基于凸偏好锥的多目标优化问题求解方法
PREAI
分享收藏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
PREAI
分享收藏分享收藏A survey on multi-objective evolutionary algorithms for the solution of the environmental/economic dispatch problems解决环境/经济调度问题的多目标进化算法综述
PREAI
分享收藏分享收藏
查看更多内容