Optimum cycle length models using atom search optimization and grasshopper optimization algorithms

Abstract

A fixed‐time traffic control system is widely used to manage the traffic flow at intersections. Cycle length has an important effect on the performance of the fixed‐time control system and the Webster model is widely used in the literature to determine the cycle length. However, when the traffic flow ratio (Y) approaches 1, the Webster model loses its effectiveness and cannot determine the cycle length when Y is above 1. In the scope of this study, it is aimed to develop models that can predict cycle length for all traffic conditions in which are Y ≤ 1 and Y ≥ 1. Two different heuristic algorithms, atom search optimization (ASO) and grasshopper optimization algorithm (GOA) were used in the development of models and the performances of these algorithms were demonstrated. The efficiency of the developed models has been demonstrated by comparing them with both Webster's model and VISTRO optimization program. Models developed in exponential, power, and quadratic forms have been able to predict cycle lengths with lower delay values by showing better performance than Webster and VISTRO. In addition, statistical results show that the ASO approach is more successful than the GOA approach.

Multi‐objective quantum atom search optimization algorithm for electric vehicle charging station planning

“…The drift factor g in the h min function is responsible for regulating the algorithm's exploration and exploitation phases. Furthermore, r represents the Euclidean distance between the atoms and σ is the collision diameter, given by Equation (26). The term K best denotes the atom selection function that selects the best atoms in the population based on the fitness value.…”

“…Ekinci et al 25 developed an improved version of ASO by integrating opposition‐based learning to enhance the diversity of the atom population. Korkmaz and Akgüngör 26 proposed a hybrid ASO algorithm by combining ASO and the grasshopper optimization algorithm (GOA) to identify the optimum cycle length in a traffic control system. Since PSO has a faster convergence rate than ASO, Zhao et al 27 combined ASO and PSO to improve the convergence speed of the basic ASO algorithm and employed it to tune the PID parameters of a hydro‐turbine governor.…”