Grey Wolf Optimizer for enhancing local search based on population

To improve the convergence rate and avoid early maturity when using Grey Wolf Optimizer (GWO) to solve the Traveling Salesman Problem (TSP), a Grey Wolf Optimizer for enhancing local search based on population (GWO-LSP) has been proposed.The grey wolf is divided into populations, and the GWO is iterated within the population and cross-genetically inherited between the populations to avoid falling into local optimum solutions,which improves the quality of the algorithm to solve the TSP problem. Meanwhile, the position update step of the grey wolf optimizer is optimized and improved, so that the iteration within populations and cross genetics between populations can be processed in parallel and the iteration speed is improved. By conducting on the TSPLib dataset experiments, the results show that the improved algorithm has the advantages of high solution quality and fast solution speed.