A multiple leaders particle swarm optimization algorithm with variable neighborhood search for multiobjective fixed crowd carpooling problem

Carpooling is a shared travel mode that can increase vehicle utilization and ease urban traffic pressure. Carpooling in a fixed group of people can not only increase the utilization rate of vehicles, but also provide better interpersonal relationships. This paper studies a multiobjective carpooling problem for people working in the same industrial park. The optimization objectives include minimizing the total mileage of vehicles, the total mileage of employees, and the extra time consumed by employees. A mathematical model was established, and a sequence code was designed. A novel type of multiple leaders particle swarm optimization algorithm MPSO-VNS combined with variable neighborhood search is proposed. The leaders are selected from the optimal solution set of particle motion according to the direction distance index. Experiments show that the MPSO-VNS algorithm can effectively solve the multiobjective carpooling problem. Compared with the six algorithms of NSGA- II , MOEA/D, PSO, MaPSO, VNS, and Two-Level VNS, MPSO-VNS can obtain a better non-dominated solution set and also has good computational efficiency.

Automated summary

This paper studies a multiobjective carpooling problem for people working in the same industrial park and proposes a novel multiple leaders particle swarm optimization algorithm MPSO-VNS which can effectively solve the problem. Experimental results show that the algorithm performs well in terms of solution efficiency and multiobjective optimization performance.