504 说说 22 标签
    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 Although BoAs have been estimated, dynamic environments can cause outdated memory issues, leading to inaccurate fitness information within subspaces.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 07:42 pm

    Mike July 3rd, 2024 at 07:41 pm

    🍜 吃饭 西苑餐厅小碗菜 + 水果捞！

    Mike July 3rd, 2024 at 12:45 am

    🚩 每日目标
    2024-07-03

    继续把接下来的文章梳理一遍，仔细优化其中的逻辑和表达问题
    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 10:09 pm

    🏆 运动 🚴 骑行 猛猛骑行20km，炫一个新品西瓜爆珠麦旋风！美中不足的就是手表骑到没电了，没记录上...

    Mike July 2nd, 2024 at 06:44 pm

    🍜 吃饭 干饭！北区椒麻鸡块饭~

    Mike July 2nd, 2024 at 03:44 pm

    💡 所思所感 刚才发生了一件让我感到后怕的事情，昨天检查实验室管理系统出现的问题时，把服务器的防火墙暂时关闭了，服务器拥有公网IP，防火墙的关闭导致能够在公网ssh到服务器，而服务器的账户密码都很简单且都拥有root权限，暴露在公网下服务器一整天都被人暴力破解，导致服务器用户名密码全部被篡改，试想如果黑客把服务器的内容全部删除，后果将多么严重。幸好只是账户名密码被篡改了，没有出现很大的损失。

    感悟有两点：

    一定要使用强密码！一定要打开防火墙！
    Mike July 2nd, 2024 at 03:24 pm

    🍜 吃饭 水果捞！

    Mike July 2nd, 2024 at 03:23 pm

    🍜 吃饭 今日午饭，东苑小碗菜！