Design of Multimodal Transport Path Optimization Model and Dual Pheromone Hybrid Algorithm

Received: 2 February 2019 Accepted: 18 August 2019 This paper aims to optimize the largescale complex multimodal transport path in an efficient and accurate manner. To this end, the genetic algorithm (GA) was embedded into the ant colony optimization (ACO), creating a dual pheromone hybrid algorithm. The GA guides and speeds up the ACO to avoid the local optimum trap and enhance the optimization ability. Considering the scale effect of transport cost, a combinatorial optimization model was established to minimize the cost under the constraints of delivery time and path capacity, and tested in largescale multimodal transport networks. The test results show that the hybrid algorithm converged to the optimal solution through 46.17% and 45.25% fewer iterations, and lowered the transport cost by 4.23% and 2.39% than the GA and ACO, respectively. In addition, the hybrid algorithm was relied on to explore the factors affecting path selection. The research findings lay a solid basis for decision-making on multimodal transport.