Artificial Bee Colony Algorithm for Optimization in Energy-saving Elevator Group Control System

Abstract

Due to the involvement of multiple unpredictable factors, optimization in Elevator Group Control System (EGCS) is a challenging task. The major optimization parameters in most previous research articles were the passengers’ average waiting time (AWT) or average journey time (AJT). Owing to the global energy crisis, however, optimizing the energy-consumption in EGCS has become a pivotal issue. In order to overcome this concern, an optimization approach utilizing Artificial Bee Colony (ABC) algorithm, which has never been applied in EGCS, is implemented in this study. Furthermore, the performance of this ABC algorithm in energy-saving EGCS is compared to that of Genetic Algorithm (GA), another popular swarm intelligence algorithm. According to the comparisons, ABC is better at minimizing energy-consumption by avoiding trapping in local minima when compared to GA. Most notably, in 100 independent simulations, this ABC algorithm exhibits substantially lower standard deviation than that of GA.