Optimization Path Programming Using Improved Multigroup Ant Colony Algorithms
The main purpose of this chapter proposes an improved multigroup ant colony optimization (IMG-ACO) algorithm to improve the traditional ant colony optimization (TACO) algorithm and traditional multigroup ant colony optimization (MG-ACO) for dealing with the optimization path problem. The TACO and MG-ACO algorithms have exhibited good performance on searching the shortest path. But on the search space, it tends to suffer from premature convergence and fall into local optimal. In this study, the IMG-ACO algorithm utilizing traditional multigroup framework and mutation mechanism performs the virtual parallel optimization algorithm. Compared with the MG-ACO, the results show that the shortest path improved by about 11.5, 16.8, and 9.1% for 60, 90, and 120 nodes, respectively. This indicates that IMG-ACO can quickly obtain the optimal or nearly optimal solutions to the path programming problem.
KeywordsACO IMG-ACO Shortest path Optimization
- 3.Pan J, Wang XS, Cheng YH (2012) Improved ant colony algorithm for mobile robot path planning. J China Univ Min Technol 41:108–113Google Scholar
- 6.Liu XH, Zhang X, Liu WJ (2008) Multi-process paths decision-making methodology based on improved max-min ant system. Comput Integr Manuf Syst 14:2414–2420Google Scholar
- 7.Ouyang J, Yan GR (2004) A multi-group ant colony system algorithm for TSP. In: Proceedings of the third international conference on machine learning and cybernetics, Shanghai, 2004, pp 26–29Google Scholar