Improved Firefly Algorithm And Its Application And Research In Vehicle Routing Problem

Firefly algorithm(FA) is a new optimization algorithm in the field of swarm intelligence optimization algorithm.It is a heuristic optimization method designed by simulating the glowing characteristics and biological behavior of fireflies in nature.Because of its strong search ability,simple algorithm structure,small parameter adjustment and other advantages,it has been widely concerned by scholars at home and abroad.At present,it is widely used in engineering,computer,management,economy and other fields.However,like many other swarm intelligence optimization algorithms,it has certain randomness.Therefore,firefly optimization algorithm will inevitably have some common problems and defects.For example: the algorithm is easy to fall into the local optimum,the late convergence speed is slow and so on,which leads to the low accuracy of the solution.Based on the analysis of firefly optimization algorithm,this paper proposes several improved algorithms from different angles and applies them to vehicle routing problem(VRP).The following is the main research work and achievements of this paper.(1)In this paper,the parameters of the original classical firefly algorithm are improved.In order to balance the global search ability and local search ability of the algorithm,this paper adopts the dynamic step size strategy that the step size gradually decreases with the number of iterations.On this basis,a control structure is added,The difference between the objective function values of the current generation and the previous generation is used to decide whether to reduce the step size to meet the needs of the next generation.If the difference between the two objective function values is small,the algorithm is considered to fall into the local optimum,and a larger step size is taken to help the algorithm jump out of the local optimum;If there is a big difference between the two generations,the position of the two generations is far away,and the global best point may be skipped,so a smaller step size is taken to improve the local search ability of the algorithm.(2)In this paper,the attraction mechanism of firefly algorithm is also adjusted accordingly.Because the initial attraction of the classical firefly optimization algorithm is 1,this paper finds that if the initial attraction is set to 1,the firefly population will converge to a local optimal solution quickly,which greatly reduces the diversity of the population.Therefore,based on a large number of experiments and previous experience,this paper sets the initial attraction as 0.4.This avoids the algorithm falling into local optimum to a certain extent,makes the firefly population tend to the optimal solution according to a certain proportion,improves the diversity of the population,and increases the search ability of the algorithm.(3)Finally,the improved firefly optimization algorithm is applied to vehicle routing problem with time windows(VRPTW).VRP is a NP hard problem,and its research object is discrete.Therefore,the individual firefly is discretized and coded again,and the decimals that may be produced in the experiment are integer and out of bounds.Through the above methods,firefly individuals can effectively express the solution of the problem,and the algorithm is tested using the internationally recognized Solomon test set.The test results verify the effectiveness and efficiency of the algorithm.