Double Improved Genetic Simulated Annealing Algorithm for Logistics Distribution Routing Optimization

Traveling Salesman Problem (TSP) is a NP-hard problem, which has important application value in various fields. This paper optimizes and improves Genetic Algorithm and Simulates Annealing Algorithms respectively, combines each other's advantages and complements each other, and develops a Double-improved genetic-simulates annealing algorithm (DIGASA), which can solve TSP with better results and shorter time. The main improvements are as follows: (1) A new fitness function is defined that can increase with the evolution process; (2) The F-adaptive function is designed to optimize the crossover and mutation probability; (3) The local optimal search is introduced to speed up the convergence of the algorithm; (4) The adaptive Metropolis criterion is adopted in the group update part to make individual changes more adaptive; (5) The cooling function is improved to increase the tempering process. Subsequently, DIGASA is tested on eight standard TSP datasets of three sizes. Experimental results show that DAGASA performs well, almost reach the known optimal solution, and the running time is short, which proves the effectiveness and rationality of DIGASA.