A Multipopulation Ant Colony System Algorithm for Multiobjective Trip Planning

Meng-Meng Sun, Zong-Gan Chen, Yuncheng Jiang, Zhi-Hui Zhan, Jun Zhang

Abstract: Trip planning service can save the time and energy of tourists for preparing a trip and provide a more comfortable and satisfying travel experience. This paper particularly considers the planning of transportation mode between point of interests (POI) and formulates a multiobjective trip planning model to simultaneously maximize the visit time in POIs, minimize the travel time between POIs, and minimize the travel fare needed for the trip. To simulate the real-world environment, the formulated model incorporates the real-world POI and transportation data crawled from Tripadvisor and Baidu Map API, respectively. To obtain efficient trip planning schemes, a multipopulation ant colony system algorithm for trip planning, abbreviated as MACS-TP, is proposed. First, MACS-TP uses two colonies to optimize the time-related objective and fare-related objective respectively, which enhances the search efficiency. Second, an archive is employed to store the nondominated solutions found by both colonies and a new pheromone global update rule is designed based on the archive to help colonies optimize their corresponding objective sufficiently. Third, an elite learning strategy is proposed to further enhance the quality of solutions in the archive. Experimental results on a real-world dataset of Guangzhou, China illustrate the effectiveness of MACS-TP.