An Effective Cooperative Coevolution–Differential Evolution Algorithm for Improving the Performance and Robustness of Ridesharing Systems with Trust Requirements

Despite the growth of ridesharing services worldwide, the adoption of ridesharing remains low compared with other modes of transportation. Five important factors influencing the willingness to consider ridesharing include time/cost, service experience, traffic/environment, privacy, and safety. Information from social networks can provide potential value for improving service experience and trustworthiness of ridesharing services. However, the problem of optimizing ridesharing decisions based on consideration of trust requirements of participating drivers and riders and other constraints in ridesharing systems poses a challenge in the development of a solution algorithm due to high computational complexity. In addition, a ridesharing optimization problem considering trust requirements is typically non-convex and non-linear with discrete decision variables, making exact methods not applicable. Metaheuristic approaches can be applied to find solutions for non-convex and non-linear discrete constrained optimization problems. The goal of this paper is to develop an effective solution algorithm to improve the performance of ridesharing systems with trust requirements. To achieve the goal of this study, we develop a variant of the Differential Evolution (DE) algorithm by combining the Cooperative Coevolution approach with the DE approach. To verify the effectiveness of the new algorithm for solving the ridesharing optimization problem with trust requirements, we conducted experiments and compared the results obtained by the new algorithm with those obtained by sixteen other competitive algorithms. Comparison with other competitive algorithms based on the experimental results shows that the proposed algorithm significantly outperforms other competitive algorithms in terms of performance and robustness. The CC–DE algorithm achieves the highest average fitness values for all test cases, whereas the other algorithms achieve the highest average fitness values for at most 80% of the test cases. The CC–DE algorithm achieves a zero standard deviation of fitness function values for all test cases, whereas the other algorithms achieve a zero standard deviation for at most 80% of the test cases. For the two largest test cases, the CC–DE algorithm outperforms the other algorithms by at least 9.246% and 12.9867%, respectively, in terms of performance.