Ridesharing is considered as one of the promising solutions for dropping the consumption of fuel and reducing the congestion in urban cities, hence reducing the environmental pollution. Route planning is a key component for the success of ridesharing systems in which multiple objectives can be optimized. The multiobjective route planning problem in multi-hop ridesharing is categorized as NP-complete. Multiobjective evolutionary algorithms have received a growing interest in solving the multiobjective optimization problems. In this work, we compare the behaviour of different multiobjective evolutionary algorithms for solving the multiobjective route planning in dynamic multi-hop ridesharing. Comparison results indicate that there is no single algorithm, as in literature, that wins all the tournaments regarding all the quality indicators. However, a subset of the algorithms is recommended with better quality and runtime.
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