| Title: | a hypervolume based approach for minimal visual coverage shortest path |
| Author: | Li Jie
; Zheng Changwen
; Hu Xiaohui
|
| Source: | 2011 IEEE Congress of Evolutionary Computation, CEC 2011
|
| Conference Name: | 2011 IEEE Congress of Evolutionary Computation, CEC 2011
|
| Conference Date: | 5-Jun-20
|
| Issued Date: | 2011
|
| Conference Place: | New Orleans, LA, United states
|
| Keyword: | Approximation algorithms
; Mathematical operators
|
| Publish Place: | United States
|
| Indexed Type: | EI
|
| ISBN: | 9781424478347
|
| Department: | (1) Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China; (2) National Key Laboratory of Integrated Information System Technology, Institute of Software, Chinese Academy of Sciences, Beijing, China
|
| English Abstract: | In this paper, the minimal visual coverage shortest path in raster terrain is studied with the proposal of a hypervolume contribution based multiobjective evolutionary approach. The main feature of the presented method is that all individuals in the population are periodically replaced by the selected non-dominated candidates in the archive based on hypervolume contribution, besides the well designed evolutionary operators and some popular techniques such as dominated relation and archive. Our algorithm may obtain well distributed Pareto set approximation efficiently, which is superior to the implementations based on the framework of NSGA-II and SMS-EMOA with respect to the hypervolume. © 2011 IEEE. |
| Content Type: | 会议论文
|
| URI: | http://ir.iscas.ac.cn/handle/311060/14381
|
| Appears in Collections: | 综合信息系统技术国家级重点实验室 _会议论文
|
| File Name/ File Size |
Content Type |
Version |
Access |
License |
|
|---|
| a hypervolume based approach for minimal visual coverage shortest path.pdf（350KB） | -- | -- | 限制开放 | -- | 联系获取全文 |
|
| Recommended Citation: | |
Li Jie,Zheng Changwen,Hu Xiaohui. a hypervolume based approach for minimal visual coverage shortest path[C]. 见:2011 IEEE Congress of Evolutionary Computation, CEC 2011. New Orleans, LA, United states. 5-Jun-20.
|
|
|
