We provide an improved algorithm
called “a neighborhood expansion tabu search algorithm based on genetic factors”
(NETS) to solve traveling salesman problem. The algorithm keeps the traditional
tabu algorithm’s neighborhood, ensure the algorithm’s strong climbing ability and
go to the local optimization. At the same time, introduce the genetic algorithm’s
genetic factor (crossover factor and variation factor) to develop new search space
for bounded domain. It can avoid the defects of the alternate search. The results
show that this optimization algorithm has improved a lot in the target of “target
value”, “convergence” and compared with traditional tabu algorithm and genetic algorithm.
Cite this paper
Wang, D. , Xiong, H. and Fang, D. (2016) A Neighborhood Expansion Tabu Search Algorithm Based On Genetic Factors. Open Journal of Social Sciences
, 303-308. doi: 10.4236/jss.2016.43037
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