Ridesharing based on a Discrete Self-adaptive Differential Evolution Algorithm

Abstract

Ridesharing provides an effective approach to reduce the number of cars, fuel consumption and greenhouse gas emissions in the environment. The problem to match passengers and drivers according to their requirements is called a ridesharing problem. Recently, many algorithms have been proposed to solve the ridesharing problem. For example, several meta-heuristic algorithms based on Differential Evolution (DE) approach have been proposed to solve the ridesharing problem. In this paper, we will propose a discrete self-adaptive Differential Evolution algorithm (SaNSDE) with neighborhood search to solve the ridesharing problem. In addition, we will compare with two variants of DE approaches to the ridesharing problem to illustrate effectiveness of the proposed SaNSDE algorithm. Several experiments have been conducted to compare the performance of SaNSDE and two variants of DE algorithms. The results indicate that the proposed SaNSDE algorithm outperforms the two variants of DE algorithms in the literature.