a new nondominated sorting genetic algorithm based to the regression line for fuzzy traffic signal optimization problem

Traffic jam is a daily problem in nearly all major cities in the world and continues to increase with population and economic growth of urban areas. traffic lights, as one of the key components at intersections, play an important role in control of traffic flow. hence, study and research on phase synchronization and time optimization of the traffic lights could be an important step to avoid creating congestion and rejection queues in a urban network. here, we describe the application of nsgaii, a multiobjective evolutionary algorithm, to optimize both vehicle and pedestrian delays in an individual intersection. results show that parameters found by improved nsgaii can be superior to those defined by a traffic engineer with respect to several objectives, including total   queue length of vehicles and pedestrians. in this paper, we improve nsgaii algorithm based to the regression line to find a paretooptimal solution or a restrictive set of paretooptimal solutions based on our solution approaches to the problem, named pdnsga (nondominated sorting genetic algorithm based on perpendicular distance). in this paper, our purpose is to present a solution methodology to obtain all paretooptimal solutions to optimize traffic signal timing and enable the decisionmakers to evaluate a greater number of alternative solutions. the proposed algorithm has the capability of searching pareto front of the multiobjective problem domain. further jobs should be concerned on the signal timing optimization method for the oversaturated coordinated intersections or smallscale road network and realfield applications with the traffic signal controller. the high speed of the proposed algorithm and its quick convergence makes it desirable for large scheduling with a large number of phases. furthermore, we have used the mean deviation from the ideal point (mdi) measure to compare the performance of the moga, pdnsga, nsgaii, and wbga by the anova method. it is demonstrated that the our proposed algorithm (pdnsga) gives better outputs than those of moga, nsgaii, and wbga in traffic signal optimization problem, statistically .