Algorithm Visualizer

逐步可视化排序、图、树算法和数据结构。
Comparisons:0
Swaps:0
Array size:0
Pseudocode — Bubble Sort
1for i = 0 to n-1:
2  for j = 0 to n-1-i:
3    if arr[j] > arr[j+1]:
4      swap(arr[j], arr[j+1])
5  mark arr[n-1-i] as sorted
Time Complexity

Repeatedly swaps adjacent elements if they are in the wrong order.

Best CaseO(n)
Average CaseO(n²)
Worst CaseO(n²)
SpaceO(1)
StableYes
Legend
Default
Comparing
Swapping
Sorted

Visual Steps

Watch each comparison, swap, and sorted element highlighted in real time.

Multiple Algorithms

Bubble, Selection, Insertion, Merge, and Quick Sort side by side.

Speed Control

Adjust animation speed from 1ms to 200ms per step with a slider.

Array Customization

Change array size from 10 to 100 elements and regenerate randomly.

关于Algorithm Visualizer

The Algorithm Visualizer animates five classic sorting algorithms — Bubble Sort, Selection Sort, Insertion Sort, Merge Sort, and Quick Sort — step by step using a live bar chart. Each comparison is highlighted in yellow, each swap in red, and each sorted element in green, making it easy to develop an intuitive understanding of how the algorithm progresses. A pseudocode panel highlights the currently executing line in sync with the animation. A complexity panel shows the best, average, and worst-case time complexity and space complexity for the selected algorithm. You can adjust the array size (10–100 elements), control animation speed (1–200 ms per step), pause at any point, and step through individual operations manually. This makes it an ideal study companion for computer science students, developers preparing for technical interviews, and educators teaching algorithms interactively.

如何使用Algorithm Visualizer

  1. Select a sorting algorithm from the dropdown — start with Bubble Sort to understand the basics before moving to more efficient algorithms.
  2. Adjust the array Size slider to choose how many bars to sort — fewer bars (10–20) make individual steps easier to follow.
  3. Set the Speed slider: lower values (1–10 ms) make the animation faster; higher values (100–200 ms) slow it down for detailed analysis.
  4. Click Generate to create a new random array, then click Start to begin the animation.
  5. Click Pause at any time, then use the Step button to advance one operation at a time while reading the highlighted pseudocode.
  6. Compare the Comparisons and Swaps counters to understand how the algorithm's efficiency relates to its theoretical Big O complexity.

常见问题

What is the most important sorting algorithm to learn?

Quick Sort is arguably the most important to understand deeply — it is the basis for most real-world sorting implementations (including JavaScript's Array.sort() and Python's sorted()). It achieves O(n log n) average performance in-place. Merge Sort is equally important for its guaranteed O(n log n) worst case and stability.

Why does Bubble Sort have O(n²) complexity?

Bubble Sort uses two nested loops — the outer loop runs n-1 times and the inner loop also runs up to n-1 times, resulting in approximately n² comparisons. For 100 elements that's 10,000 operations; for 10,000 elements that's 100,000,000. This quadratic growth makes it impractical for large datasets.

What does "stable" mean for a sorting algorithm?

A stable sort preserves the relative order of equal elements. For example, if you sort a list of names by last name and two people have the same last name, a stable sort guarantees they remain in their original order. Merge Sort, Insertion Sort, and Tim Sort are stable; Quick Sort and Heap Sort are not.

When would you use Insertion Sort over Quick Sort?

Insertion Sort is O(n) on nearly-sorted data, making it faster than Quick Sort for small arrays (typically under 10–20 elements). That is why Tim Sort (used by Python and Java) uses Insertion Sort for small subarrays and Merge Sort for larger ones — combining their strengths.

How does Merge Sort achieve O(n log n) in the worst case?

Merge Sort always divides the array in half (log n levels of recursion) and merges each level in O(n) time, giving exactly O(n log n) regardless of the input order. Its main downside is O(n) extra space for the temporary merge buffers.

Algorithm Visualizer 完全使用指南与免费在线工具

无需安装、完全免费、纯前端本地运行的高效 Algorithm Visualizer 工具

用于快速解决：逐步可视化排序、图、树算法和数据结构。。无需安装复杂软件，打开网页即可立即使用，极速且安全。

为什么选择 Metoolzy 的 Algorithm Visualizer？

  • 秒级即时响应: 极致性能优化，输入即可见结果。
  • 数据绝对安全: 纯浏览器本地计算，数据绝不上传服务器。
  • 完全免费使用: 无需注册登录，无任何隐形消费。

三步快速上手

  1. 打开 Metoolzy Algorithm Visualizer 工具页面。
  2. 在输入区粘贴或上传您的数据。
  3. 即时查看处理结果并点击复制或下载。

功能对比表

对比项 Metoolzy Algorithm Visualizer 普通在线工具 本地客户端软件
使用成本 永久免费 有次数限制或收费 需购买授权
隐私保护 纯本地内存运行 数据需上传云端 本地运行

适用人群与应用场景

  • 软件开发人员日常调试与数据转换
  • UI/UX 设计师资产处理
  • 运营与办公人员效率提升

常见问题解答 (FAQ)

处理的数据会被记录或保存吗？

绝不会。所有运算均在您的浏览器本地完成，没有任何数据回传。