Parallel combination of genetic algorithm and ant algorithm based on dynamic K-means cluster

Authors:
Jianli Ding;Wansheng Tang;Liuqing Wang
Affiliations:
Institute of Systems Engineering, Tianjin University, Tianjin, China and Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin, China;Institute of Systems Engineering, Tianjin University, Tianjin, China;Institute of Systems Engineering, Tianjin University, Tianjin, China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Year:
2006

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Abstract

Many actual project problems generally belong to large-scale TSP, The large-scale TSP as a famous NP-hard problem will be faced with the dual challenges of the optimization performance and the CPU run-time performance for any single algorithm. In fact, the optimal solution is not pursued overwhelmingly in actual projects, but it needs to meet certain optimization efficiency. This paper reduces the problem's complexity based on the idea of "divide and rule". We use the method of K-Means cluster to divide the nearest neighbor quickly. Then, we employ parallel computing method to all divided areas by using combination of genetic algorithm and ant algorithm (GAAA). Finally, we globally link all the subsets using the method of K centers connect. The results of simulations show that its complexity has been greatly reduced and we can quickly obtain a satisfactory solution to the large-scale problem. It is one effective way to solve the large-scale complex problems.