A simulation-based multi-objective genetic algorithm (SMOGA) for transportation network design problem

An energy-efficient scheduling approach to improve the utilization of regenerative energy for metro systems

“…It was firstly initiated by Holland (1975) by mimicking the natural evolution process. Due to its extensive generality, strong robustness, high efficiency and practical applicability, genetic algorithm has been widely adopted as a numerical method for solving many transportation research problems, including the build-operate-transfer network design problem (Chen et al, 2006;Chen and Subprasom, 2007), stochastic multiobjective network design problem (Chen et al, 2010), reliability-based land use and transportation optimization (Yim et al, 2011), arterial traffic signal offset optimization (Hu and Liu, 2013), electric vehicle charging station (Dong et al, 2014), traffic restriction network design problem (Shi et al, 2014), and metro optimization problem (Yang et al, 2012(Yang et al, , 2015Xu et al, 2014). In this paper, we apply genetic algorithm to solve the following optimization model without the equality constraint.…”

Bi-objective bimodal urban road network design using hybrid metaheuristics

“…This method was proposed by Osyczka and Kundu (1995), and used by Chen et al (2006) for a multi-objective CNDP. The method works with the relative distance between a generated solution and the members of the Pareto-optimal set.…”

“…The method works with the relative distance between a generated solution and the members of the Pareto-optimal set. According to Chen et al (2006), the basic idea is to assign a fitness value to each solution according to the distance measure with reference to the existing non-dominated solutions obtained in the previous generation, and a higher fitness value is assigned to a solution if it is farther away from the existing non-dominated solution set.…”

“…GA has been successfully applied in the network design problems (e.g., Yin 2000Yin , 2002Drezner and Salhi 2002;Drezner and Wesolowsky 2003;Chen et al 2006Chen et al , 2010a. The algorithm proposed here is a multi-objective hybrid version of GA and SA adopted from our previous work (Miandoabchi et al 2010).…”

Bi-objective network optimization for spatial and temporal coordination of multiple highway construction projects

“…GA has been applied to many optimization problems by overcoming the combinatorial explosion of certain problems. Recently, GA has received considerable attention thanks to their applicability to multi-objective optimization problems (Chen et al, 2006(Chen et al, , 2010Fwa et al, 2000;Knowles et al, 2000;Pilson et al, 1999;Sarker et al, 2002Sarker et al, , 2004Gen et al, 2004;). GA maintains a population of potential solutions from generation to generation, and does the multiple directional and global searches.…”