504 说说 22 标签
🌃 每日总结
    Mike July 12th, 2024 at 08:30 am

    🌃 每日总结 🏆 运动 🚴 骑行
    2024-07-11
    老师回学校了，跟老师沟通了一下，机制要越简单越好（比如双层排斥机制），不要想的太复杂，逻辑要通顺。老师改的内容我改得差不多了。同时骑行了25km，回去的路上遇见了一只小猫咪。

    Mike July 11th, 2024 at 03:34 pm

    🌃 每日总结 🏆 运动 🚴 骑行
    2024-07-10
    本日完成了老师对论文提的意见的修改，同时和汉华进行了骑行的运动，走了全新的路线，到了学校->八一路->东湖绿道湖中道->梨园->湖北省博物馆->楚河汉街->水果湖->武汉大学凌波门->八一路->学校

    Mike July 10th, 2024 at 01:28 am

    🌃 每日总结
    2024-07-09
    论文修改了两大处，需要把时间融入公式中，想了很久不知道怎么表达

    Mike July 9th, 2024 at 01:09 am

    🌃 每日总结 🏆 运动 🏊 游泳 🍜 吃饭
    2024-07-08
    今天舟车劳顿，从家里到学校了，下午睡了一下午，校园卡找不到了，补了一张校园卡10r，现在24小时自助服务还挺方便，晚上尝试去游了一次泳，结果都不能浮起来😑😑😑😑😑，好难！晚上按照老师的意见改了会儿论文

    Mike July 6th, 2024 at 11:56 pm

    🌃 每日总结
    2024-07-06
    和Liao去宜春中学、袁山公园走了走、去润达抓了娃娃、晚上吃了胡姐麻辣，挺好吃的，每周运动2/2达标！

    Mike July 5th, 2024 at 11:25 pm

    🌃 每日总结
    2024-07-05
    今日回家，去了一趟公安局，公安备案的事情搞定了，本网站的ICP备案号为: 赣ICP备2024036828号-1；公安备案号为: 赣公网安备36090202000252

    Mike July 5th, 2024 at 02:10 am

    🌃 每日总结
    2024-07-04

    去协和医院看了病，左耳的听力恢复了不少，但是还需要再去一次；论文改完了，跟老师说了；把博客加了一个垃圾评价过滤系统，为公安备案做准备。
    Mike July 4th, 2024 at 12:31 am

    🌃 每日总结
    2024-07-03

    继续改了论文的部分：

    在解空间分割中，第一句话总起本段，强调为什么要将解空间划分成子空间，然后再如何划分，用什么方法划分。
    Partitioning the solution space into multiple subspaces helps quantify the population's search behavior (e.g., which subspaces were sampled and how many times they were sampled), thereby providing guiding assistance for population searches (such as biasing searches towards subspaces with fewer samples). At the same time, subspaces can serve as basic units, making it easier to construct and manage BoAs. This paper utilizes the k-d tree method to partition the subspaces. The k-d tree is a binary tree data structure that partitions points in k-dimensional space using hyperplanes. Each node in the tree represents a point, and the left and right subtrees represent points on either side of the hyperplane. The canonical method \cite{Bentley1975} is often used to create a balanced k-d tree by selecting a median point along the cutting axis.

    定义不确定度中，总起句强调设置了一种不确定度机制，并解释了原因
    In order to determine whether it is worth expending computational resources on a particular subspace, this paper proposes a property called uncertainty. The higher the uncertainty, the more worthwhile it is to sample that subspace, while the lower the uncertainty, the less value there is in sampling that subspace.

    在估算吸引域中，第一句话提出估计BoAs的重要意义，再进行解释
    Estimating BoAs is crucial as it forms the foundation for developing population search strategies. After generating multi-populations, each subpopulation focuses on its search area for rapid convergence, evolving using the particle swarm optimization algorithm with inertia weight\cite{Shi1998}. This evolution process leaves behind substantial historical data, including individual positions, fitness values, etc.
    If these BoAs can be learned from evolutionary historical data, the algorithm can quantitatively control the populations' search range based on BoAs and select the regions that need to be searched, avoiding blind and repetitive searches.

    在多样性增强里第一句话说明多样性丢失的现象，接着解释增加多样性的重要性（为什么要增加多样性）

    Over time, the diversity of populations decreases, eventually leading to the loss of search capability. Although BoAs have been estimated, dynamic environments can cause outdated memory issues, leading to inaccurate fitness information within subspaces. Consequently, the estimations may not be sufficiently accurate. To maintain the accuracy of BoAs, it is essential to continuously update the information within the subspaces. In other words, making the adjustment of diversity critically important.

    还剩下实验、总结部分没有修改，明天修改完成跟老师说一下。

    Mike July 3rd, 2024 at 12:43 am

    🌃 每日总结 💡 所思所感
    2024-07-02

    今日早上起床稍晚，明日需改正！今日晚上骑行运动了，骑了大概22km，体力没有之前经常运动那么好了，还是要坚持运动。本周运动指标达成1/2！老师早上发了一条消息，强调写论文："每个段落第一句话交代主题、论点，后面在解释，表达要具体明确。 仔细琢磨每个段落之间的逻辑性和段内的逻辑性。"，想想也确实是这样的，根据老师的意见我大致修改了introduction的内容和proposed framework的部分内容。

    在Introduction中：
    提出两个关键科学问题：搜索会有盲目性及后果（重复搜索某一个区域），种群的搜索范围未与峰的形状适配及后果（峰的丢失，后面补充过大/过小的搜索范围的存在的问题）。
    Traditional multi-population algorithms rely on the current state of each population to make decisions. This approach clearly has limitations, for two reasons:
    \textbf{1) the search process of a population involve a high level of blindness, leading to repeated search in the same region.}
    The population is only regenerated randomly, and the re-evolution process may converge to regions that have been searched before, resulting in redundant search of the same area.
    \textbf{2) The search range of each population does not adapt to the shape of the peaks, causing the loss of peaks that are tracked}.
    If the search range is too large, small but crucial local optima may be missed. Conversely, if the search range is too small, exploration may be limited to nearby areas, reducing population diversity.

    引出我们自己的研究方法，细节更详细的补充了，包括吸引域的估计使用了什么样的信息，不是简单的说使用历史信息，用什么方法估计的吸引域。
    To address the aforementioned issues, this paper proposes a multi-population framework for solving dynamic optimization problems based on space partition (MPFD). The algorithm partitions the solution space using a k-d tree, dividing it into a user-defined number of subspaces. Each subspace is assigned an uncertainty attribute; the higher the uncertainty of a subspace, the greater the probability that the algorithm will conduct searches within it. During the evolution process of multiple populations, the algorithm records the evolutionary information within each subspace, including the positions and fitness values of individuals. The algorithm fully utilizes this information by employing a subspace clustering method to group subspaces into basins of attraction (BoAs)\cite{baketaric2021attraction}. Based on these BoAs, mechanisms for exploration and exploitation are proposed. Additionally, a dual-layer exclusion mechanism and a population hibernation mechanism are introduced to save computational resources.

    在Proposed Framework中：
    第一句话总起本段，提出想法：可以从什么样的历史数据中学习什么样的问题结构，能解决什么样的问题
    The characteristics of the problem can be learned from the historical data of the evolution. During the evolution process, populations accumulate valuable historical data, including the positions and fitness of individuals. If we can extract the structural characteristics of the problem, such as the number of peaks, their locations, and their BoAs, from these historical data, we can guide the algorithm to sample within the learned better regions (e.g., the subspace where BoA is located). This increases the likelihood of finding better solutions more quickly. Additionally, we can quantitatively control the algorithm's exploration and exploitation in different regions, significantly reducing the issue of blindly repeating searches in certain areas.

    第二段提出了本文"从什么样的历史数据中学习什么样的问题结构"的具体做法：本文基于上述想法，是通过种群在子空间中遗留的个体位置和适应值信息，通过子空间聚类的方法，学习BoAs，是如何解决前面提出的两个关键科学问题。
    This study leverages historical data from population evolution, including the positional distribution and fitness information of individuals within the population. By employing subspace clustering method, BoAs of the problem are estimated. Based on these BoAs, the search behavior of the population is controlled. Specifically, areas outside the BoAs are explored to identify any undiscovered peaks, while exploitation is performed within the BoAs to enhance search precision. This approach overcomes the issue of blind repetitive searches found in traditional methods. Additionally, by precisely quantifying and controlling the search range according to the BoAs, the success rate of peak tracking is increased.

    第三段，综合上述两段提出的思考，提出本文的研究方法，总体介绍本文的研究方法。引出下文。也是按照总起句，然后解释的方式，行文中间也着重关注了逻辑和细节（比如利用什么信息？什么方法估计的吸引域？都详细的阐述了）

    Based on the above idea, this paper introduces a dynamic optimization framework based on solution space partition. The proposed framework first uses the k-d tree to divide the solution space into multiple subspaces. Then, multiple populations are generated based on subspaces, BoAs are estimated using the subspace clustering method, based on the historical information retained within the subspace, specifically the position and fitness of individuals within the populations. Exploration is conducted within the overlap regions, while exploitation is carried out within BoAs. Additionally, supplementary populations are generated within BoAs that are not covered by existing populations, in order to avoid losing track of peaks. To save limited computing resources, this paper proposes a dual-layer exclusion mechanism to prevent populations from repeatedly exploring the same region. Additionally, populations are put into hibernation when they converge. The detailed description for each component is given in the following sections.

    很难改，改的时候，感觉要思考很久，我觉得第一段和第二段还是稍微有一点点重复的地方，明天问问老师怎么改。

    Mike July 2nd, 2024 at 12:42 am

    🌃 每日总结 💡 所思所感
    2024-07-01

    完成，加上了基金和作者地址可以先投TEVC试试，大师兄说审稿就一个月，看看意见和Danial联系了，他把论文提交时间往后延长了，目前在忙于找工作，比较忙没看，因为收到李老师的电话，说论文还需要再改，再改就再改吧，老师说不要讲大道理，要讲透，点出动机，不要写模棱两可的词语，尽量不要创造新的概念。比如造成了很大的影响，什么影响? 要讲清楚。又比如因果关系要对应起来，逻辑要严谨，不要从一个原因推断出毫无关系的结果，甚至是不要绕弯子，不要间接推断出结果，不能从第一步直接推断出第三步，该写的东西要详细写，写过的东西不要啰嗦，不要仅局限于表面的东西，还要深入挖掘，比如说现有的方法存在盲目性，盲目在哪里? 要点出来，盲目在总是重复搜索一块区域。总之就是逻辑要严谨，思路要清晰，该写的一定要写，啰嗦的就省略。老师叫我再梳理一遍