diff --git a/src/pages/about.js b/src/pages/about.js
index 04fe4ec..198ed43 100644
--- a/src/pages/about.js
+++ b/src/pages/about.js
@@ -16,35 +16,34 @@ const AboutPage = () => {
         <>
             <Navbar />
             <div style={containerStyle}>
-                <h2>About this project</h2>
-                <p>
-                    Traveling Salesman Problem is blah blah blah Lorem ipsum dolor sit amet, consectetur adipiscing elit,
-                    sed do eiusmod tempor incididunt ut labore et dolore magna aliqua quaerat voluptatem. Ut enim aeque
-                    doleamus animo, cum corpore dolemus, fieri tamen permagna accessio potest, si aliquod aeternum et
-                    infinitum impendere malum nobis opinemur. Quod idem licet transferre in voluptatem, ut postea
-                    variareli voluptas distinguique possit, augeri amplificarique non possit.
-                </p>
-                <p>
-                    Ullus investigandi veri, nisi inveneris, et quaerendi defatigationo turpis est, cum esset accusata
-                    et vituperata ab Hortensio. Qui liber cum et mortem contemnit, qua qui est imbutus quietus esse
-                    numquam potest. Praeterea bona praeterita grata recordatione renovata delectant. Est autem situm
-                    in nobis ut et voluptates et dolores nasci fatemur e corporis.
-                </p>
-                <p>
-                    Ullus investigandi veri, nisi inveneris, et quaerendi defatigationo turpis est, cum esset accusata
-                    et vituperata ab Hortensio. Qui liber cum et mortem contemnit, qua qui est imbutus quietus esse
-                    numquam potest. Praeterea bona praeterita grata recordatione renovata delectant. Est autem situm
-                    in nobis ut et voluptates et dolores nasci fatemur e corporis voluptatibus et doloribus --
-                    itaque concedo, quod modo dicebas, cadere causa, si qui.
-                </p>
+                <h1>About the Traveling Salesman Problem (TSP)</h1>
+                <h2>What is the Traveling Salesman Problem?</h2>
+                <p>The Traveling Salesman Problem (TSP) is a classic optimization problem in computer science and operations research. It asks:</p>
+                <p><em>"Given a list of cities and the distances between them, what is the shortest possible route that visits each city exactly once and returns to the starting point?"</em></p>
+                <p>TSP has applications in logistics, manufacturing, and route planning. However, solving it efficiently becomes difficult as the number of cities increases.</p>
 
-                <h4>Created by</h4>
+                <h2>Solving TSP with Blind Search</h2>
+                <p>Blind search methods explore solutions without using problem-specific knowledge:</p>
                 <ul>
-                    <li>64010823 วีรภัทร อินอุดม</li>
-                    <li>64010543 พงศ์ภีระ วงศประสิทธิพร</li>
-                    <li>64011106 ณรงค์พล กิจรังสรรค์</li>
-                    <li>64011160 นนทัช มุกลีมาศ</li>
+                    <li><strong>Brute Force:</strong> Generates all routes and picks the shortest. It guarantees an optimal solution but has a factorial time complexity.</li>
+                    <li><strong>Breadth-First Search (BFS):</strong> Explores routes level by level but grows exponentially in complexity.</li>
+                    <li><strong>Depth-First Search (DFS):</strong> Traverses full paths before backtracking but may not be optimal.</li>
                 </ul>
+                <p>Blind search methods are inefficient for large-scale TSP instances.</p>
+
+                <h2>Solving TSP with Heuristic Search</h2>
+                <p>Heuristic search methods use problem-specific knowledge to find solutions efficiently:</p>
+                <ul>
+                    <li><strong>Greedy Algorithm:</strong> Chooses the nearest unvisited city but does not guarantee the best solution.</li>
+                    <li><strong>A* Search:</strong> Uses cost estimation to optimize the search.</li>
+                    <li><strong>Genetic Algorithms:</strong> Uses evolution-based optimization.</li>
+                    <li><strong>Simulated Annealing:</strong> Uses randomization to escape local optima.</li>
+                    <li><strong>Ant Colony Optimization:</strong> Mimics how ants find paths efficiently.</li>
+                </ul>
+                <p>These methods balance accuracy and computational efficiency, making them suitable for real-world applications.</p>
+
+                <h2>Conclusion</h2>
+                <p>The Traveling Salesman Problem is a fundamental challenge in optimization and AI. While blind search guarantees the optimal route, its cost is too high for large problems. Heuristic search algorithms provide practical alternatives that yield near-optimal solutions efficiently.</p>
             </div>
         </>
     );