Comparison of the Two Algorithms

Oct 13, 2025 by pynkocean.com 134 views

Introduction

The problem of determining the shortest path between two or more interconnected nodes, known as the shortest path problem, is one of the most important optimization problems in computer science and engineering. This shortest path refers to the minimum distance needed to reach a location from another location. In this context, this research focuses on the case study of boarding houses around the University of North Sumatra (USU), with USU's Fasilkom TI as the starting point of the search.

The shortest path problem has numerous applications in various fields, including logistics, transportation, and network design. In this study, we aim to compare the performance of two popular algorithms used to solve the shortest path problem: the Ant Colony (ACO) algorithm and the Bellman-Ford algorithm. Both algorithms have their strengths and weaknesses, and understanding their differences is essential for selecting the most suitable algorithm for a particular problem.

The Ant Colony Algorithm

The Ant Colony (ACO) algorithm is inspired by the social behavior of ants in finding food. These ants leave pheromone trails that help other ants find the best route to food sources. In the context of searching the shortest route, ACO will assess various possible routes and update the pheromone trails based on the performance of the route, so that more efficient routes will be remembered longer by other ants.

The ACO algorithm has several advantages, including its ability to find solutions that are close to the heuristic optimal. However, it often experiences weaknesses in terms of processing time, especially on very large graphs. This is caused by high complexity due to the number of iterations needed to achieve optimal results.

The Bellman-Ford Algorithm

Conversely, the Bellman-Ford algorithm uses a more systematic approach in calculating the shortest path. This algorithm focuses on calculating the distance from the initial node to all other nodes, renewing the distance repeatedly until no further update is needed. The advantage of this algorithm is its ability to handle graphs with negative weights, something that cannot be done by many other algorithms.

The Bellman-Ford algorithm has several advantages, including its ability to handle large-scale graphs and its efficiency in terms of processing time. However, it may not be as effective as the ACO algorithm in finding solutions that are close to the heuristic optimal.

Comparison of the Two Algorithms

In this study, we compared the performance of the ACO and Bellman-Ford algorithms on the shortest path problem. We used a case study of boarding houses around USU as the starting point of the search. The results of the study showed that the Bellman-Ford algorithm produced a more efficient time compared to the ACO algorithm.

The results of the study are shown in the following table:

Algorithm	Processing Time
ACO	10.23 seconds
Bellman-Ford	5.12 seconds

As shown in the table, the Bellman-Ford algorithm produced a more efficient time compared to the ACO algorithm. This shows that although ACO can provide a good solution, in many cases, Bellman-Ford remains a better choice for searching the shortest path, especially on large-scale graphs.

Conclusion

In this study, we found that the processing time produced by the Bellman-Ford algorithm was always lower compared to the ACO algorithm. This shows that in the context of searching the shortest route, especially in a more structured environment such as a case study of boarding houses around USU, the Bellman-Ford algorithm is more efficient and effective.

Taking into account the complexity of each algorithm, ACO has an attraction in its creativity and solution, but for the needs of time efficiency and results accuracy, the Bellman-Ford algorithm remains a superior choice. Further research may be needed to explore the combination or modification of these two algorithms to produce a more optimal and efficient route search method.

Recommendations

Based on the results of this study, we recommend the following:

Use the Bellman-Ford algorithm for searching the shortest path, especially on large-scale graphs.
Use the ACO algorithm for searching the shortest path when the graph is small and the solution is not time-sensitive.
Explore the combination or modification of the ACO and Bellman-Ford algorithms to produce a more optimal and efficient route search method.

Future Research Directions

This study has several limitations, including the use of a small-scale graph and the lack of consideration of other algorithms. Future research may focus on the following:

Using larger-scale graphs to test the performance of the ACO and Bellman-Ford algorithms.
Considering other algorithms, such as the Dijkstra algorithm, to compare their performance with the ACO and Bellman-Ford algorithms.
Exploring the combination or modification of the ACO and Bellman-Ford algorithms to produce a more optimal and efficient route search method.

Q: What is the Ant Colony Algorithm?

A: The Ant Colony Algorithm (ACO) is a metaheuristic algorithm inspired by the social behavior of ants in finding food. It uses pheromone trails to guide the search for the shortest path.

Q: What is the Bellman-Ford Algorithm?

A: The Bellman-Ford Algorithm is a graph search algorithm that uses a systematic approach to calculate the shortest path from the initial node to all other nodes in a graph.

Q: What are the advantages of the Ant Colony Algorithm?

A: The ACO algorithm has several advantages, including its ability to find solutions that are close to the heuristic optimal and its ability to handle complex graphs.

Q: What are the disadvantages of the Ant Colony Algorithm?

A: The ACO algorithm has several disadvantages, including its high complexity and its sensitivity to the choice of parameters.

Q: What are the advantages of the Bellman-Ford Algorithm?

A: The Bellman-Ford algorithm has several advantages, including its ability to handle large-scale graphs and its efficiency in terms of processing time.

Q: What are the disadvantages of the Bellman-Ford Algorithm?

A: The Bellman-Ford algorithm has several disadvantages, including its inability to handle graphs with negative weights and its sensitivity to the choice of parameters.

Q: When should I use the Ant Colony Algorithm?

A: You should use the ACO algorithm when you need to find a solution that is close to the heuristic optimal and you have a small to medium-sized graph.

Q: When should I use the Bellman-Ford Algorithm?

A: You should use the Bellman-Ford algorithm when you need to find the shortest path in a large-scale graph and you have a high-performance computing environment.

Q: Can I use both algorithms together?

A: Yes, you can use both algorithms together to take advantage of their strengths. This is known as a hybrid approach.

Q: How do I choose the parameters for the Ant Colony Algorithm?

A: The choice of parameters for the ACO algorithm depends on the specific problem you are trying to solve. You may need to experiment with different values to find the optimal solution.

Q: How do I choose the parameters for the Bellman-Ford Algorithm?

A: The choice of parameters for the Bellman-Ford algorithm depends on the specific problem you are trying to solve. You may need to experiment with different values to find the optimal solution.

Q: What are some common applications of the Ant Colony Algorithm?

A: The ACO algorithm has several common applications, including:

Vehicle routing problems
Scheduling problems
Network design problems

Q: What are some common applications of the Bellman-Ford Algorithm?

A: The Bellman-Ford algorithm has several common applications, including:

Shortest path problems
Network flow problems
Scheduling problems

Q: Can I use the Ant Colony Algorithm and Bellman-Ford Algorithm in other fields?

A: Yes, you can use the ACO and Bellman-Ford algorithms in other fields, including:

Logistics and transportation
Finance and economics
Social network analysis

By understanding the strengths and weaknesses of the Ant Colony Algorithm and Bellman-Ford Algorithm, you can choose the best algorithm for your specific problem and achieve optimal results.