# DEMO "Toolbox"
## Differential Evolution for Multiobjective Optimization
These codes were developed by [Fillipe Goulart](http://orcslab.cpdee.ufmg.br/index.php/current-members/46-fillipe-goulart-silva-mendes) ([fillipe.gsm@gmail.com](fillipe.gsm@gmail.com)) during his M.Sc. at Universidade Federal de Minas Gerais, under the mentoring of Prof. [Felipe Campelo](http://orcslab.cpdee.ufmg.br/index.php/faculty/5-felipe-campelo) ([fcampelo@ufmg.br](fcampelo@ufmg.br)).
The _Octave-Matlab_ folder contains the implementations for Octave (which should work on Matlab too). The following algorithms are implemented:
- A posteriori methods (without preferences):
– DEMO [1]: the regular DEMO with non-dominated sorting;
– IBEA [2]: DEMO using indicators instead.
- A priori or interactive (with preferences):
– R-DEMO [3]: R-NSGA-II but using the DEMO instead;
– PBEA [4]: IBEA but using a reference point;
– PAR-DEMO(nds) [5]: the method proposed by us, using nondominated sorting;
– PAR-DEMO(ε) [5]: the same method, but using indicators instead.
Fillipe's M.Sc. thesis is available [here](http://ppgee.ufmg.br/defesas/1120M.PDF), and contains an extensive review on multiobjective optimization and preference-based methods. It also contains a more extensive description and discussion of the Preference-based Adaptive Region-of-interest (PAR) framework.
If you use these codes in any way, please cite our paper [5]:
@article{Goulart2016,
doi = {10.1016/j.ins.2015.09.015},
url = {http://dx.doi.org/10.1016/j.ins.2015.09.015},
year = {2016},
month = {feb},
publisher = {Elsevier {BV}},
volume = {329},
pages = {236--255},
author = {Fillipe Goulart and Felipe Campelo},
title = {Preference-guided evolutionary algorithms for many-objective optimization},
journal = {Information Sciences}
}
The description of the methods and examples of use are available in the [Read me.pdf](https://github.com/ORCSLab/DEMO/blob/master/Read%20me.pdf) file.
### References
1. T Robic and B Filipic. DEMO: Differential evolution for multiobjective optimization. Evolutionary Multi-Criterion Optimization, 520–533, 2005.
1. Eckart Zitzler and S Kunzli. Indicator-based selection in multiobjective search. Parallel Problem Solving from Nature-PPSN VIII, (i):1–11, 2004.
1. Kalyanmoy Deb, J. Sundar, Rao N. Udaya Bhaskara, and Shamik Chaudhuri. Reference Point Based Multi-Objective Optimization Using Evolutionary Algorithms. International Journal of Computational Intelligence Research, 2(3):273– 286, 2006.
1. Lothar Thiele, Kaisa Miettinen, PJ Korhonen, and Julian Molina. A preference- based evolutionary algorithm for multi-objective optimization. Evolutionary Computation, 17(3):411–436, 2009.
1. Fillipe Goulart and Felipe Campelo. Preference-guided evolutionary algorithms for many-objective optimization. Information Sciences, 329:236 – 255, 2016. Special issue on Discovery Science.
没有合适的资源?快使用搜索试试~ 我知道了~
多目标优化的差分进化及其变体_MATLAB_代码_下载
共24个文件
m:22个
md:1个
pdf:1个
1.该资源内容由用户上传,如若侵权请联系客服进行举报
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
2.虚拟产品一经售出概不退款(资源遇到问题,请及时私信上传者)
版权申诉
5星 · 超过95%的资源 8 下载量 126 浏览量
2022-06-22
06:21:16
上传
评论 3
收藏 307KB ZIP 举报
温馨提示
实现了以下算法: 后验方法(无偏好): – DEMO [1]:具有非支配排序的常规 DEMO; – IBEA [2]:DEMO 使用指标代替。 先验或交互(带有偏好): – R-DEMO [3]:R-NSGA-II,但使用 DEMO 代替; – PBEA [4]:IBEA,但使用参考点; – PAR-DEMO(nds) [5]:我们提出的方法,使用非支配排序; – PAR-DEMO(ε) [5]:相同的方法,但使用指标代替。 更多详情、使用方法,请下载后阅读README.md文件
资源推荐
资源详情
资源评论
收起资源包目录
DEMO-master (1).zip (24个子文件)
DEMO
Octave-Matlab
DTLZ
dtlz4.m 1KB
dtlz_distance.m 2KB
dtlz5.m 1KB
dtlz_range.m 1KB
dtlz2.m 1KB
dtlz3.m 1KB
dtlz7.m 1KB
dtlz_ideal_nadir.m 1KB
dtlz1.m 1KB
dtlz6.m 1KB
includepaths.m 323B
Algorithms
demo_opt.m 13KB
ndset.m 1KB
asf.m 2KB
demo_par_ind.m 14KB
demo_ibea_opt.m 13KB
crowdingdistance.m 1KB
demo_par_nds.m 13KB
demo_pbea_opt.m 13KB
rdemo_opt.m 15KB
find_roi.m 2KB
fstandardize.m 958B
Read me.pdf 267KB
README.md 3KB
共 24 条
- 1
快撑死的鱼
- 粉丝: 1w+
- 资源: 9154
下载权益
C知道特权
VIP文章
课程特权
开通VIP
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功
- 1
- 2
前往页