Elevator Group-Control Policy With Destination Registration Based On Hybrid Genetic Algorithms

This paper firstly introduces the developing background of the elevator group controlsystem and analysis its mathematical properties and the puzzles that traditional controlstrategies facing. Considering the new developing intelligent bionic algorithm is superior atdealing with the optimization of the complex control system, applying it into the field ofelevator group control.This dissertation focuses on the intelligent bionic algorithm which has been rapidlydeveloped in recent years and its future research prospect. In this paper,an advanced PSO isproposed to overcome premature searching by Standard Particle Swarm Optimization (SPSO)algorithm.The Simulation results verifies the validity and high efficiency of the improvedalgorithm. And then we introduce the thought to genetic algorithm (GA) and translate theoptimized objective into the index function of the elevator group control system (EGCS), thenumerical simulation shows that the algorithm is effective in practical computation.At the last part, a set of virtual simulation software of EGCS is implemented by VisualBasic.this software using manually input to realize the simulation of static elevator groupcontrol; we also edit passenger flow model which can automatically generating the trafficflow in the typical time by choosing the different building types and the traffic patterns ormanually entering the traffic percentage and the passenger arrival rate,finally applying thedispatching strategy to get the optimized elevator schedule. In the end of the paper, we furtherdetail the design principles of the compositions and design flow.