Artificial fish swarm algorithm based traffic route searching method

Abstract

The invention relates to an artificial fish swarm algorithm based traffic route searching method. The artificial fish swarm algorithm based traffic route searching method comprises the following steps of setting a maximum number of iterations of an algorithm to be Max, the size of a fish swarm to be n, the dimension of parameters to be d, a state position of the i fish to be (Xi1, Xi2, Xi3 to Xid), the food concentration of the i fish to be f (Xi), a shortest route value to be as the following formula; step 1 initializing algorithm parameters; S2 perform random initialization on the fish swarm within solution space; S3 calculating a target value which is as the following formula of artificial fish through fuzzy simulation and extracting a current optimal solution Xgbest; S4 judging whether the algorithm achieves the maximum number Max of iterations or not, outputting the optimal solution on an annunciator board and ending the algorithm if yes and executing steps from a S5 to S9 if no; S5 enabling individuals in the fish swarm to randomly swim; S6 executing a foraging operation; S7 executing a bunching operation; S8 executing a tailgating operation; S9 enabling the number t of iterations is equal to t+1 and executing the S3. The artificial fish swarm algorithm based traffic route searching method can rapidly search a most convenient traffic route.

Description

technical field [0001] The invention relates to a traffic route search method based on artificial fish swarm algorithm. Background technique [0002] The traditional traffic route search method generally selects the shortest path between any two locations in the city, and the driver needs to search for the path with the shortest travel time. In real life, due to traffic jams and other accidents, the shortest path The route is not necessarily the one with the shortest travel time. According to this situation, some intelligent algorithms are usually used to solve the shortest path. [0003] One is particle swarm optimization (PSO, full name in English: particle swarm optimization). PSO simulates the predation behavior of a flock of birds. The solution to each optimization problem is a bird in the search space, which is called a "particle". Particles have an fitness value determined by the optimized function, and each particle has a speed that determines the direction and dis...

AI Technical Summary

Problems solved by technology

Genetic algorithm is good at global search, but its local search ability is obviously insufficient, the speed of searching for the optimal solution or satisfactory solution is slow, and there are problems such as easy to fall into the local optimal solution and premature convergence caused by excessive selection pressure.

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