For a long time, many researchers have investigated the continuous network design problem (CNDP) to distribute equitably additional capacity between selected links in a road network, to overcome traffic congestion in urban roads. In addition, CNDP plays a critical role for local authorities in tackling traffic congestion with a limited budget. Due to the mutual interaction between road users and local authorities, CNDP is usually solved using the bilevel modeling technique. The upper level seeks to find the optimal capacity enhancements of selected links, while the lower level is used to solve the traffic assignment problem. In this study, we introduced the enhanced differential evolution algorithm based on multiple improvement strategies (EDEMIS) for solving CNDP. We applied EDEMIS first to a hypothetical network to show its ability in finding the global optimum solution, at least in a small network. Then, we used a 16-link network to reveal the capability of EDEMIS especially in the case of high demand. Finally, we used the Sioux Falls city network to evaluate the performance of EDEMIS according to other solution methods on a medium-sized road network. The results showed that EDEMIS produces better solutions than other considered algorithms, encouraging transportation planners to use it in large-scale road networks.
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