Abstract A discrete mussels wandering optimization (DMWO) algorithm is designed for solving the traveling salesman problem (TSP). An evaluation function and a measure operator indicating differences among mussels are given within DWMO framework. To overcome the defect caused by the overall discrete routine adjustment, a local routine adjustment strategy based on 2-opt is adopted to enhance the searching ability of the algorithm. The experiment is conducted on several standard TSPLIB testing data of different sizes. Compared with discrete glowworm swarm optimization and ant colony optimization adopting 2-opt, the results show the competitive performance of the proposed algorithm in terms of solution consistency, accuracy and the number of iterations.
About author:: HAN Wei(Corresponding author), born in 1975, Ph.D., associate professor. His research interests include artificial intelligence.ZHANG Zicheng, born in 1991, master student. His research interests include data mining and artificial intelligence.
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2016, Vol. 29 
