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The objective of the limited-stop service design and frequency setting problem (LSDFSP) is to realize the operation of a transit route with a set of elaborate service patterns and corresponding frequencies that can minimize the total social cost of the users and operators. In practice, these different patterns and frequencies involve a trade-off between the user and operator requirements, and this aspect cannot be fully clarified by a single-objective optimization problem. Therefore, in this study, the LSDFSP is considered at the network level and formulated as a multi-objective optimization problem with competing objectives of minimizing the user and operator costs. The cooperative coevolutionary multi-objective evolutionary algorithm is redesigned to collaboratively optimize the service patterns and frequencies. A prioritization method is proposed to separately incorporate different types of unsatisfied demand as critical indicators, prompting the algorithm to dig deeper into valuable genes and evolve more feasible solutions. The proposed algorithm is tested on a small network and a real intricate network. It is noted that higher frequencies increased the fleet size and decreased the users’ waiting time and in-vehicle time. Furthermore, skipping more stations reduced the fleet size and users’ in-vehicle time, while increasing the users’ waiting time and number of transfers. The computational results indicated that the proposed algorithm can suitably incorporate the trade-offs and generate an accurate set of Pareto-optimal solutions.

The transit network design and frequency setting problem (TNDFSP) is a complex combinatorial optimization problem. Generally, the nature of multiobjective in TNDFSP has not attracted enough attention, and the frequency setting is directly embedded as a subproblem to generate a unique set of frequencies for a given transit network, ignoring trade-off solutions among multiple objectives with different sets of frequencies. In this study, the problem is formulated as a multiobjective model with two conflicting objectives of minimizing passengers’ and operators’ costs. Moreover, we establish two populations to simultaneously optimize networks and frequencies. Also a cooperative coevolutionary multiobjective evolutionary algorithm (CCMOEA) is developed to collaboratively coevolve these two populations along multiple objectives. Unsatisfied demand is embedded into the individual prioritization process, and infeasible individuals can be retained instead of being replaced arbitrarily, driving the evolution to gradually generate more feasible solutions. The proposed CCMOEA is tested on the well-known Mandl’s benchmark. The results show that our algorithm can efficiently produce a comprehensive set of high-quality trade-off solutions. These solutions perform well with lower waiting time, competitive in-vehicle travel time and number of transfers, resulting in lower user costs than previously published results in the same fleet size.