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