Future Generation Computer Systems
Using Genetic Algorithms to Optimize ACS-TSP
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
A new hybrid heuristic approach for solving large traveling salesman problem
Information Sciences—Informatics and Computer Science: An International Journal
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 07
MST Ant Colony Optimization with Lin-Kerninghan Local Search for the Traveling Salesman Problem
ISCID '08 Proceedings of the 2008 International Symposium on Computational Intelligence and Design - Volume 01
Multi-Direction Searching Ant Colony Optimization for Traveling Salesman Problems
CIS '08 Proceedings of the 2008 International Conference on Computational Intelligence and Security - Volume 02
Information Sciences: an International Journal
WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
The traveling salesman: computational solutions for TSP applications
The traveling salesman: computational solutions for TSP applications
IEEE Computational Intelligence Magazine
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
On the Invariance of Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An annealing framework with learning memory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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The travelling salesman problem (TSP) is a classic problem of combinatorial optimization and is unlikely to find an efficient algorithm for solving TSPs directly In the last two decades, ant colony optimization (ACO) has been successfully used to solve TSPs and their associated applicable problems Despite the success, ACO algorithms have been facing constantly challenges for improving the slow convergence and avoiding stagnation at the local optima In this paper, we propose a new hybrid algorithm, cooperative ant colony system and genetic algorithm (CoACSGA) to deal with these problems Unlike the previous studies that regarded GA as a sequential part of the whole searching process and only used the result from GA as the input to the subsequent ACO iteration, this new approach combines both GA and ACS together in a cooperative and concurrent fashion to improve the performance of ACO for solving TSPs The mutual information exchange between ACS and GA at the end of each iteration ensures the selection of the best solution for the next round, which accelerates the convergence The cooperative approach also creates a better chance for reaching the global optimal solution because the independent running of GA will maintain a high level of diversity in producing next generation of solutions Compared with the results of other algorithms, our simulation demonstrates that CoACSGA is superior to other ACO related algorithms in terms of convergence, quality of solution, and consistency of achieving the global optimal solution, particularly for small-size TSPs.