A set-based differential evolution algorithm for QoS-oriented and cost-effective ridesharing

Abstract

The rapid proliferation of private cars brings serious problems such as traffic congestion and air pollution. Ridesharing provides a promising way to alleviate these issues by allocating riders to drivers with similar itineraries. In this work, we focus on a QoS-oriented and cost-effective ridesharing problem and propose an efficient algorithm to solve the problem. First, we formulate the ridesharing model and encode the solutions based on sets considering both service quality and cost. Then, we develop a set-based differential evolution algorithm to search the global optimum for the formulated problem. From the algorithm aspect, we specifically design new operators, such as the greedy initialization, the inter-vehicle mutation, and the route-sensitive selection, to enhance the performance of differential evolution for dealing with the ridesharing problem. The experimental results show that our method outperforms the state-of-the-art methods on metropolis transport datasets.