Abstract
Shuffled frog-leaping algorithm (SFLA) is a new memetic meta-heuristic algorithm with efficient mathematical function and global search capability. Traveling salesman problem (TSP) is a complex combinatorial optimization problem, which is typically used as benchmark for testing the effectiveness as well as the efficiency of a newly proposed optimization algorithm. When applying the shuffled frog-leaping algorithm in TSP, memeplex and submemeplex are built and the evolution of the algorithm, especially the local exploration in submemeplex is carefully adapted based on the prototype SFLA. Experimental results show that the shuffled frog leaping algorithm is efficient for small-scale TSP. Particularly for TSP with 51 cities, the algorithm manages to find six tours which are shorter than the optimal tour provided by TSPLIB. The shortest tour length is 428.87 instead of 429.98 which can be found cited elsewhere.
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