A Cooperative Ant Colony System and Genetic Algorithm for TSPs

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 success folly used to solve TSPs and their associated applicable problems Despite the success. AGO algorithms have been facing constantly challenges for improving die 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 recorded GA as a sequential part of the whole searching process and only used the result from GA as the input to the subsequent AGO iteration. there new approach combines both GA and ACS together in a cooperation ye and concurrent fashion to improve the performance of AGO for solving TSPs The mutual information enhance between ACS and GA at the end of each iteration ensures the selection of the best solution lot the next round. which accelerates the convenience The cooperative approach also creates a better chance 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 demonstrate: that CoACSGA is superior to other AGO related algorithms in terms of convergence. quality of solution, and consistency of achieving the global optimal solution. particularly for small-size TSPs