An improved multi-objective particle swarm optimization algorithm and its application in vehicle scheduling

Due to the lack of diversity of the initial population, the multi-objective particle swarm optimization algorithm easily falls into the local optimal value during the iterative process. The method of piecewise logistic chaotic map is introduced to increase the randomness of initial population. A disturbance variable is used to weaken the dependency on global optimal value. A segmented maintenance of the external file is used to select the particle which is more representative for the population. A monitoring selection mechanism is used to improve the population jump out of local optimum. The strategy for eliminating the final particle one by one is used to clip the external file. The validity of the proposed algorithm is proved by comparing with the other algorithms on the test function. And the proposed algorithm has been used to solve the vehicle routing problem.