An Improved ACS Algorithm for the Solutions of Larger TSP Problems
This work addresses the problem of efficiently solving large combinatorial optimization problems for researchers in computer science, representing an incremental improvement over prior methods.
The paper tackles the challenge of solving large traveling salesman problems by proposing a modified ant colony system algorithm called RB-ACS, which combines ant colony optimization with genetic algorithm parallelism to achieve significantly better performance than existing best-known algorithms.
Solving large traveling salesman problem (TSP) in an efficient way is a challenging area for the researchers of computer science. This paper presents a modified version of the ant colony system (ACS) algorithm called Red-Black Ant Colony System (RB-ACS) for the solutions of TSP which is the most prominent member of the combinatorial optimization problem. RB-ACS uses the concept of ant colony system together with the parallel search of genetic algorithm for obtaining the optimal solutions quickly. In this paper, it is shown that the proposed RB-ACS algorithm yields significantly better performance than the existing best-known algorithms.