Vehicle route optimization method with time window constraint based on improved particle swarm optimization (PSO)

Abstract

The invention relates to a vehicle route optimization method with time window constraint based on improved particle swarm optimization (PSO). The method comprises the following steps of: step 10, initially setting parameters; step 20, constructing a particle swarm; step 30, decoding particles according to a decoding rule; step 40, calculating fitness for a distribution route obtained by decoding; step 50, searching an individual optimal state and a group optimal state to select the group optimal state Pg, which is the optimal route of the vehicle route problem under the current iteration conditions, determining the condition that the optimal position searched by the particle swarm is the optimal route in the current state, entering step 601 if a stop condition is not reached, and otherwise, entering step 603; step 601, updating the state; step 602, introducing crossover operator operation, and entering step 30 to repeat particle decoding; and step 603, stopping iteration, and outputting the optimal route result of the vehicle route problem.

Description

technical field [0001] The invention belongs to the field of computer science, relates to a method for solving the optimal vehicle routing problem with time window constraints, and also relates to the application of genetic algorithms and particle swarm optimization algorithms in the field of artificial intelligence. Background technique [0002] Vehicle routing problem (Vehicle Routing Problems, VRP) refers to a certain number of customers, each with a different quantity of goods demand, the distribution center provides goods to customers, and a team is responsible for distributing goods, organizing appropriate driving routes, so that vehicles have Through them sequentially, under certain constraints (such as cargo demand, delivery volume, delivery time, vehicle capacity limit, mileage limit, time limit, etc.), the goal of a certain problem (such as the shortest distance, cost, etc.) is achieved. minimum, as little time as possible, as few vehicles as possible, etc.). Cons...

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Problems solved by technology

Although the exact algorithm can get the exact solution, but the amount of calculation is very large, generally increases exponentially with the increase of the problem scale, the solution time is too long, and can only solve the simple VRP problem with a limited number of nodes, while the traditional heuristic algorithm, although The calculation time is shortened, and the amount of calculation is also reduced, but often only an approximate solution close to the optimal solution can be obtained, and the scope of application can only be limited to small-scale VRP problems. When the number of nodes increases, the solution accuracy is often very low. Difference

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