GA Based Hybrid Fuzzy Rule Optimization Approach for Elevator Group Control System

Abstract

Elevators are the essential transportation tools in high buildings so that Elevator Group Control System (EGCS) is developed to dynamically layout the schedule of elevators in a group. In this study, a fuzzy rules based intelligent elevator group control system has been proposed in which the structure of rules including the related parameters are generated optimally based on the traffic data so as to maximize service quality. In literature, the fuzzy related approaches have been applied in EGCS but the fuzzy rules were all pre-defined. However, how to create the most suitable fuzzy rule set in EGCS for dispatching elevators more efficiently and economically are never discussed in literature. The aim of the proposed approach is to minimize the average waiting time at peak hours as well as to minimize the power energy at off-peak hours by using the proposed fuzzy rule based ECGS. Moreover, there are many decision variables are considered in the GCGS to provide the most appropriate elevator assignment whenever any hall call is given. These variables include the number of elevators, traffic flow, direction, passenger preferences (for instance, department stores, hospitals, hotels, and office buildings), congestion and VIP priority floor, etc. In this study, a fuzzy rule based elevator-dispatching approach has been proposed for the EGCS in which the fuzzy rules and related parameters are derived optimally by using genetic algorithm based on the historical elevator transportation data. The experimental results show that the performance of the proposed approach is superior to these of traditional approaches in literatures.

Performance Analysis of Particle Swarm Optimization and Genetic Algorithm in Energy-Saving Elevator Group Control System

“…The first and most commonly implemented metaheuristic algorithm for EGCS is GA. The primary concept of EGCS was mentioned by Fujino et al [15] in 1997, where the authors implemented the EGCS employing GA. Following that in 2001 and 2003, Tyni and Ylinen [16,17] implemented two GA approaches.…”

Fuzzy System Based on Two-Step Cascade Genetic Optimization Strategy for Tobacco Tar Prediction

“…Mathematically, this algorithm generates best subtractive clustering parameter as part of generated structure we can represent by Eq. (13).…”

“…Genetic learning algorithm was the base for representation of knowledge using new extended fuzzy rule models [12,13]. One example of technical application is how to handle and process high throughput data using integration of GA-FUZZY algorithm with Hadoop Map-Reduce technique in order to solve gene classification problems [14].…”