Abstract
Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.
Highlights
One of the most popular combinatorial optimization problems is the traveling salesman problem (TSP) [1]
All experiments were conducted on the symmetric TSP
General, the results indicate that the structure of the proposed algorithm, which depends on the concepts of embedding simulated annealing, mutation operation, and local search procedure, achieved the balance between diversification and kroC100 20749 kroD100 21294 kroE100 22068
Summary
One of the most popular combinatorial optimization problems is the traveling salesman problem (TSP) [1]. TSP is a representative of variety of combinatorial problems. It has been studied for the last 40 years. Metaheuristic algorithms are formally defined as algorithms that inspired by nature and biological behaviors. They produce high-quality solutions by applying a robust iterative generation process for exploring and exploiting the search space efficiently and effectively. Metaheuristic algorithms seem to be a hot and promising research areas [5]. They can be applied to find near-optimal solutions in a reasonable time for different combinatorial optimization problems [6]
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