Soft time window vehicle path optimization method based on GA-PSO of leader

Abstract

The invention relates to a soft time window vehicle path optimization method based on GA-PSO of a leader, and the method comprises the steps: S1, initializing population parameters: the population size is N, the GA population size is N1, the PSO population size is N2, and N = N1 + N2; s2, initializing GA and PSO respectively; s3, enabling the number of iterations K to be equal to 1; s4, calculating a fitness value according to the fitness function; s5, selecting the optimal individuals of the two algorithms as the leader of the next generation; s6, judging whether K<Maxgen is true or not, if yes, executing the step S7, and otherwise, executing the step S14; s7, enabling B1 individuals with relatively high GA iteration right values and the Leader to be subjected to crossover operation; s8,performing mutation operation on all individuals of the GA population; s9, selecting the first N1 individuals as the next generation according to the right values of the two generations; s10, making the first B2 particles with the high fitness value to fly towards the Leader; s11, performing mutation operation on the remaining N2-B2 particles with relatively poor fitness values of the PSO; s12, selecting the first N2 particles as the next generation according to the right values of the two generations; s13, enabling K to be equal to K + 1, and executing the step S4; and S14, outputting an optimal solution and an optimal fitness function value.

Description

technical field [0001] The invention belongs to the field of logistics transportation, and in particular relates to a soft time window vehicle route optimization method based on leader-based GA-PSO. Background technique [0002] The Vehicle Routing Problem (VRP) was first proposed by Dantzing and Ramster in 1959. It refers to one or more distribution centers that distribute goods to multiple customers with different needs. Under constraints such as driving distance and maximum load capacity, a series of goals are achieved, such as the shortest driving distance and minimum consumption. In the process of logistics distribution, it is often affected by many factors such as weather changes, traffic congestion, and uneven distribution of distribution outlets. At the same time, it must meet the customer's time window requirements. corresponding punishment. How to arrange the best distribution plan has become the focus and difficulty in logistics distribution. By rationally form...

AI Technical Summary

Problems solved by technology

When there are too many distribution points and vehicles, the scale of data to be processed will increase, and the complexity of the problem will increase. It is often difficult to obtain accurate distribution results. How to use a method to obtain more accurate solutions in less time has been become a research hotspot

Although the research on the problem of vehicle routing is very comprehensive, in the case of large scale and large number of vehicles, the existing algorithms still cannot quickly and accurately find the optimal route for delivery, and some algorithms are prone to fall into local extremum. Some algorithms are easy to mature and have no memory

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