摘要：

针对电梯群控系统(EGCS)中用户乘梯体验与系统能耗不理想的问题, 提出一种基于改进粒子群的电梯群控多目标优化调度算法。首先, 针对系统控制目标的复杂性, 建立以乘客候梯时间、乘梯时间、长时候梯和系统能耗为指标的多目标优化模型, 通过线性加权求和的方法设计系统综合评价函数, 改变权重值以适应不同的交通模式。其次, 引入灰狼优化(GWO)算法以解决粒子群优化(PSO)算法易陷入局部最优解的问题, 将灰狼-粒子群混合优化算法应用到多目标调度系统中。仿真结果表明, 该混合算法能够有效地减少用户的平均乘、候梯时长和电梯启停次数, 提升了电梯群控系统的综合性能。

关键词: 群控电梯, 多目标优化, 软件仿真, 灰狼优化算法, 粒子群优化算法

Abstract:

An improved particle swarm-based multi-objective optimization scheduling algorithm for elevator group control is proposed to address unsatisfactory user experience and system energy consumption in Elevator Group Control Systems(EGCS). First, considering the complexity of the system control objectives, a multi-objective optimization model is established using indicators such as passenger waiting time, riding time, long waiting time, and system energy consumption. Using the linear weighted summation method to design the system's comprehensive evaluation function, changing the weights can adapt to different traffic patterns. Second, the Grey Wolf Optimization (GWO) algorithm is introduced to address the issue of the Particle Swarm Optimization (PSO) algorithm being prone to falling into local optimal solutions. The grey wolf-particle swarm hybrid optimization algorithm is applied to a multi-objective scheduling system. Simulation results show that this hybrid algorithm can effectively reduce the average riding and waiting times for users and the number of elevator starts and stops, thereby enhancing the elevator group control system's overall performance.

Key words: group controlled elevator, multi-objective optimization, software simulation, Grey Wolf Optimization (GWO) algorithm, Particle Swarm Optimization (PSO) algorithm