An improvement of fruit fly optimization algorithm for solving traveling salesman problems

Abstract

TSP (Traveling Salesman Problem, TSP) is a classic combinatorial optimization problem and nowadays it is a hot topic to find a high precision algorithm with short solving time. As its search space increases with the number of cities, it often requires a lot of computation time to find the optimal solution in a large space. So a new TSP solving algorithm is proposed based on an improved fruit fly optimization algorithm. Aiming at solving these shortcomings of standard fruit fly optimization algorithm, such as easily plunging into local optimal and low convergence-rate, mutation operator is introduced, which improves the diversity of the population and prevents premature. The strategy of adaptive variable step size is adopted, which increases search efficiency effectively. Finally, the improved fruit fly optimization algorithm is verified to be efficient, comparing with standard fruit fly optimization algorithm and particle swarm optimization algorithm benchmarked against TSPLIB.