<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[future12356的博客]]></title><description><![CDATA[记录一些看的论文和基础算法吧 (*￣3￣)╭]]></description><link>https://blog.youkuaiyun.com/future12356</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; future12356]]></copyright><item><title><![CDATA[Model-based evolutionary algorithms: a short survey 阅读笔记]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/123063429</link><guid>https://blog.youkuaiyun.com/future12356/article/details/123063429</guid><author>future12356</author><pubDate>Tue, 22 Feb 2022 10:54:57 +0800</pubDate><description><![CDATA[Model-based evolutionary algorithms: a short survey
1、摘要
本综述通过考虑使用模型的三种不同动机，简要回顾了一些有代表性的MBEAs。
第一，使用模型最常见的动机是估计候选解的分布。
第二，在进化多目标优化中，使用模型的动机之一是建立从目标空间到决策空间的逆模型。
第三，当解决计算量大的问题时，模型可以作为适应度函数的替代品。
2、引言
MBEAs的思想是用 [ML模型](Machine Learning Models) 取代操作算子，候选解被用作每一代]]></description><category></category></item><item><title><![CDATA[E-CSO-2021-阅读笔记]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/120674365</link><guid>https://blog.youkuaiyun.com/future12356/article/details/120674365</guid><author>future12356</author><pubDate>Sat, 09 Oct 2021 16:46:02 +0800</pubDate><description><![CDATA[An Efficient Competitive Swarm Optimizer for Solving Large-Scale Multi-objective Optimization Problems  

Li Y., Li L., Lin Q., Ming Z. (2021) An Efficient Competitive Swarm Optimizer for Solving Large-Scale Multi-objective Optimization Problems. In: Huang]]></description><category></category></item><item><title><![CDATA[iWOF阅读笔记]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/120659664</link><guid>https://blog.youkuaiyun.com/future12356/article/details/120659664</guid><author>future12356</author><pubDate>Fri, 08 Oct 2021 20:54:36 +0800</pubDate><description><![CDATA[An Improved Weighted Optimization-based Framework for Large-scale MOPs  

J. Zheng, L. Li, Q. Lin and Z. Ming, “An Improved Weighted Optimization-based Framework for Large-scale MOPs,” 2021 IEEE Congress on Evolutionary Computation (CEC), 2021, pp. 2156-21]]></description><category></category></item><item><title><![CDATA[LSMOF/D阅读笔记]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/120647566</link><guid>https://blog.youkuaiyun.com/future12356/article/details/120647566</guid><author>future12356</author><pubDate>Fri, 08 Oct 2021 10:54:03 +0800</pubDate><description><![CDATA[Large-scale Multiobjective Optimization via Problem Decomposition and Reformulation  

L. Li, C. He, R. Cheng and L. Pan, “Large-scale Multiobjective Optimization via Problem Decomposition and Reformulation,” 2021 IEEE Congress on Evolutionary Computation ]]></description><category></category></item><item><title><![CDATA[锦标赛选择算法及 matlab 实现]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/116306505</link><guid>https://blog.youkuaiyun.com/future12356/article/details/116306505</guid><author>future12356</author><pubDate>Fri, 30 Apr 2021 16:54:15 +0800</pubDate><description><![CDATA[锦标赛选择（Tournament Selection）
每次从种群中取一定数量（n）的个体(放回抽样)，选择其中适应度较好的进入子代种群。重复该操作直到种群规模到和原来的种群规模一样。几元锦标赛就代表一次性从总体中抽取几个个体，然后从中选择最优的个体保留到下一代种群。
步骤如下：


确定每次选择的个体数量N。（二元锦标赛选择即选择2个个体）


从种群中随机选择N个个体(每个个体被选择的概率相同) ，根据每个个体的适应度值，选择其中适应度值最好的个体进入下一代种群。


重复步骤(2)多次（重复次数为种群]]></description><category></category></item><item><title><![CDATA[轮盘赌选择算法及matlab实现]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/116269651</link><guid>https://blog.youkuaiyun.com/future12356/article/details/116269651</guid><author>future12356</author><pubDate>Thu, 29 Apr 2021 15:13:02 +0800</pubDate><description><![CDATA[《进化计算的理论和方法》 P10
一、准备步
计算群体中每个个体 xix_ixi​ 被选择的概率，即公式（1）
pi=Fit(xi)∑j=1NFit(xj)
p_i=\dfrac{Fit\left( x_i \right)}{\sum_{j=1}^N{Fit\left( x_j \right)}}
pi​=∑j=1N​Fit(xj​)Fit(xi​)​
注意到，pi≥0p_i\ge 0pi​≥0，且 p1+p2+...+pn=1p_1+p_2+...+p_n=1p1​+p2​+...+pn​=1，故可以将 ]]></description><category></category></item><item><title><![CDATA[LSMOF-2019 阅读笔记]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/116056330</link><guid>https://blog.youkuaiyun.com/future12356/article/details/116056330</guid><author>future12356</author><pubDate>Fri, 23 Apr 2021 15:12:01 +0800</pubDate><description><![CDATA[LSMOF-2019 阅读笔记
Accelerating Large-scale Multi-objective Optimization via Problem Reformulation

He C , Li L , Tian Y , et al. Accelerating Large-Scale Multiobjective Optimization via Problem Reformulation[J]. IEEE Transactions on Evolutionary Computation,]]></description><category></category></item><item><title><![CDATA[WOF-MMOPSO-RDG-2020 阅读笔记]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/115667603</link><guid>https://blog.youkuaiyun.com/future12356/article/details/115667603</guid><author>future12356</author><pubDate>Tue, 13 Apr 2021 15:01:37 +0800</pubDate><description><![CDATA[A random dynamic grouping based weight optimization framework for large-scale multi-objective optimization problems

Liu R ,  Liu J ,  Li Y , et al. A random dynamic grouping based weight optimization framework for large-scale multi-objective optimization ]]></description><category></category></item><item><title><![CDATA[MOEA/D-RDG 2016 阅读笔记]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/115640318</link><guid>https://blog.youkuaiyun.com/future12356/article/details/115640318</guid><author>future12356</author><pubDate>Mon, 12 Apr 2021 20:51:53 +0800</pubDate><description><![CDATA[MOEA/D-RDG
A Random-Based Dynamic Grouping Strategy for Large Scale Multi-objective Optimization

A. Song, Q. Yang, W. N. Chen, and J. Zhang, “A random-based dynamic grouping strategy for large scale multi-objective optimization,” in IEEE Congress on Evolu]]></description><category></category></item><item><title><![CDATA[SSVG-2020-阅读笔记]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/115630808</link><guid>https://blog.youkuaiyun.com/future12356/article/details/115630808</guid><author>future12356</author><pubDate>Mon, 12 Apr 2021 17:33:16 +0800</pubDate><description><![CDATA[A Symmetric Points Search and Variable Grouping Method for Large-scale Multi-objective Optimization

Tang D ,  Wang Y ,  Wu X , et al. A Symmetric Points Search and Variable Grouping Method for Large-scale Multi-objective Optimization[C]// 2020 IEEE Congre]]></description><category></category></item><item><title><![CDATA[iLSMOA-2020 - 阅读笔记]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/115523949</link><guid>https://blog.youkuaiyun.com/future12356/article/details/115523949</guid><author>future12356</author><pubDate>Thu, 08 Apr 2021 16:33:54 +0800</pubDate><description><![CDATA[Iterated Problem Reformulation for Evolutionary Large-Scale Multiobjective Optimization

C. He, R. Cheng, Y. Tian and X. Zhang, “Iterated Problem Reformulation for Evolutionary Large-Scale Multiobjective Optimization,” 2020 IEEE Congress on Evolutionary Co]]></description><category></category></item><item><title><![CDATA[PlateMO3.0各函数的属性及方法]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/115051280</link><guid>https://blog.youkuaiyun.com/future12356/article/details/115051280</guid><author>future12356</author><pubDate>Sun, 21 Mar 2021 16:33:29 +0800</pubDate><description><![CDATA[SOLUTION.m
属性
dec 解的决策变量
obj 解的目标值
方法
decs 得到多个解的决策变量矩阵
objs 得到多个解的目标值矩阵
best 在多个解中得到可行解和非支配解

PROBLEM.m
属性:
N 	  种群大小
M 	  目标数
D 	  决策变量数
PF      帕累托前沿图像
optimum 问题的最优值
方法:
CalObj     计算解的目标值
GetOptimum 生成问题的最优解
GetPF 	 生成帕累托前沿的图像
DrawDec    在决策空间中显示一个种群
]]></description><category></category></item><item><title><![CDATA[优快云 图片缩小居中]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/112805354</link><guid>https://blog.youkuaiyun.com/future12356/article/details/112805354</guid><author>future12356</author><pubDate>Tue, 19 Jan 2021 16:20:03 +0800</pubDate><description><![CDATA[优快云 图片缩小居中]]></description><category></category></item><item><title><![CDATA[CCLSM - 2018]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/112758327</link><guid>https://blog.youkuaiyun.com/future12356/article/details/112758327</guid><author>future12356</author><pubDate>Sun, 17 Jan 2021 21:47:14 +0800</pubDate><description><![CDATA[CCLSM  协同进化 大规模多目标优化算法]]></description><category></category></item><item><title><![CDATA[Fast Interdependency Identification (FII) - 2017]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/112724465</link><guid>https://blog.youkuaiyun.com/future12356/article/details/112724465</guid><author>future12356</author><pubDate>Sat, 16 Jan 2021 21:16:19 +0800</pubDate><description><![CDATA[快速相互依赖识别算法 FII]]></description><category></category></item><item><title><![CDATA[编辑视角下，论文摘要、引言、结论怎么写？]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/112686248</link><guid>https://blog.youkuaiyun.com/future12356/article/details/112686248</guid><author>future12356</author><pubDate>Fri, 15 Jan 2021 19:46:58 +0800</pubDate><description><![CDATA[编辑视角下，论文摘要、引言、结论怎么写？

姚巍，朱金才。关于摘要、引言及结尾写法的思考 [A]. 华东地区高等院校自然科学学报编辑协会。学报编辑论丛（第十一集）[C].: 华东地区高校学报研究会，2003:3.

根据笔者的工作经验，多数作者在论文写作时，对论文的摘要、引言、结尾的写作，或马马虎虎，或不得要领，大大影响了论文的被关注度和影响力。其实，科技论文不仅仅是向人们展示最新科技成果，更重要的是把科研和应用联系起来，在向人们展示科技成果的同时，指出科技成果的应用前景。科技论文的摘要、引言、结尾（结束语]]></description><category></category></item><item><title><![CDATA[CCGDE3 - 2013]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/112669019</link><guid>https://blog.youkuaiyun.com/future12356/article/details/112669019</guid><author>future12356</author><pubDate>Fri, 15 Jan 2021 15:18:00 +0800</pubDate><description><![CDATA[协同进化解决大规模多目标优化问题
CCGDE3]]></description><category></category></item><item><title><![CDATA[立贴为证，开始写博文~]]></title><link>https://blog.youkuaiyun.com/future12356/article/details/112632034</link><guid>https://blog.youkuaiyun.com/future12356/article/details/112632034</guid><author>future12356</author><pubDate>Thu, 14 Jan 2021 19:44:45 +0800</pubDate><description><![CDATA[今天开始动手写博客吧，注册 优快云 好几年了，一直以来都是白嫖别人的文章。心里也想往外输出一些知识，择日不如撞日，就在今天以写总结的时机写一下关于读研期间看的论文吧！
我看的文章很多，却也很是杂乱，但主要还是以兴趣——算法与数据结构；和研究方向——大规模多目标进化优化算法为主。以后的更新就以这些为主吧(&gt;▽&lt;)！
哈哈，写的第一篇博文居然是这样子的，很是新奇～(￣▽￣～)(～￣▽￣)～
哦，对了，2021-1-14 19:44立贴为证，应该不会太监了吧(⊙ˍ⊙) ？

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