Multi-vehicle cooperative carpooling path optimization method based on improved fruit fly algorithm
A path optimization and fruit fly algorithm technology, applied in the field of intelligent transportation, can solve problems such as the difficulty of traveling by taxi, achieve the effect of improving diversity, reducing seat vacancy rate, and enhancing the ability to escape from local optimum
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Embodiment 1
[0044] see figure 1 , the method for solving the multi-vehicle cooperative carpool route optimization problem proposed by the present invention, its specific steps are as follows:
[0045] 1. Establish goals and establish optimization models
[0046] The symbols used in this example to solve the multi-vehicle collaborative carpooling route optimization problem are as follows:
[0047] C: Taxi collection;
[0048] N: the number of people in the taxi;
[0049] R: the collection of paths for taxis to complete carpooling orders;
[0050] D: The location of the taxi and the collection of departure and destination associated with the passenger order;
[0051] S ir : Binary decision variable, indicating whether the i-th passenger is in the sub-path r, i∈D, r∈R;
[0052] h ijr : Binary decision variable, indicating whether the taxi transports passenger i and passenger j continuously in the rth sub-path, i, j∈D, r∈R;
[0053] d ij : the distance between the location of the tax...
Embodiment 2
[0076] In this embodiment, in combination with the route optimization problem of multi-vehicle cooperative carpooling in a taxi company, the present invention is used to find the optimal solution or suboptimal solution that satisfies the constraint conditions.
[0077] 1. Problem overview
[0078] According to the above technical solution, a certain taxi company is used as an example for illustration. Randomly generate 27 passenger orders for testing. The distance between the location of the taxi and the origin of the passenger, as well as the distance between the origin and destination of the passenger and the origin and destination of other passengers obeys the uniform distribution on [5km, 20km]. The nuclear load N of the taxi is 4. The experiment was carried out on the Win10 system platform, Intel processor with 3.7GHz main frequency, 4GB memory and Matlab R2014b development environment.
[0079] 2. Comparison with other evolutionary algorithms
[0080] In order to ver...
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