Annealing Ant Colony Optimization with Mutation Operator for Solving TSP. (24th November 2016)

• Record Type: Journal Article
• Authors: Mohsen, Abdulqader M.
• Other Names: Masciari Elio Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2016(2016)
• Publication Date: 2016-11-24
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2016/8932896
• Languages: English
• ISSNs: 1687-5265

Abstract

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.