Elevator Group Supervisory Control System Using Genetic Network Programming with Macro Nodes and Reinforcement Learning

2007 ◽  
Vol 127 (8) ◽  
pp. 1234-1242 ◽  
Author(s):  
Jin Zhou ◽  
Lu Yu ◽  
Shingo Mabu ◽  
Kotaro Hirasawa ◽  
Jinglu Hu ◽  
...  
Keyword(s):  
Control System ◽  
Reinforcement Learning ◽  
Supervisory Control ◽  
Genetic Network ◽  
Network Programming ◽  
Supervisory Control System ◽  
Genetic Network Programming

Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming with Ant Colony Optimization with Evaporation

2007 ◽  
Vol 11 (9) ◽  
pp. 1149-1158 ◽  
Author(s):  
Lu Yu ◽  
◽  
Jin Zhou ◽  
Shingo Mabu ◽  
Kotaro Hirasawa ◽  
...  
Keyword(s):  
Control System ◽  
Ant Colony Optimization ◽  
Supervisory Control ◽  
Hybrid Algorithm ◽  
Directed Graphs ◽  
Genetic Network ◽  
Ant Colony ◽  
Network Programming ◽  
Supervisory Control System ◽  
Genetic Network Programming

Recently, Artificial Intelligence (AI) technology has been applied to many applications. As an extension of Genetic Algorithm (GA) and Genetic Programming (GP), Genetic Network Programming (GNP) has been proposed, whose gene is constructed by directed graphs. GNP can perform a global searching, but its evolving speed is not so high and its optimal solution is hard to obtain in some cases because of the lack of the exploitation ability of it. To alleviate this difficulty, we developed a hybrid algorithm that combines Genetic Network Programming (GNP) with Ant Colony Optimization (ACO) with Evaporation. Our goal is to introduce more exploitation mechanism into GNP. In this paper, we applied the proposed hybrid algorithm to a complicated real world problem, that is, Elevator Group Supervisory Control System (EGSCS). The simulation results showed the effectiveness of the proposed algorithm.

Elevator Group Supervisory Control System Using Genetic Network Programming with Functional Localization

2006 ◽  
Vol 10 (3) ◽  
pp. 385-394 ◽  
Author(s):  
Toru Eguchi ◽  
◽  
Jin Zhou ◽  
Shinji Eto ◽  
Kotaro Hirasawa ◽  
...  
Keyword(s):  
Control System ◽  
Supervisory Control ◽  
Genetic Network ◽  
Graph Structure ◽  
Network Programming ◽  
Practical Applications ◽  
Real World Problem ◽  
Functional Localization ◽  
Supervisory Control System ◽  
Genetic Network Programming

Genetic Network Programming (GNP) having a directed graph structure has been proposed as a new method of evolutionary computation. Recently, GNP has been applied to elevator group supervisory control system (EGSCS), a real-world problem, to demonstrate its applicability and effectiveness. Its previous study considers the known and fixed traffic flow, however, it is changed dynamically with time in real elevator systems. Therefore, an EGSCS with dynamic adaptive control considering such changes should be studied for practical applications. In this paper, we have applied GNP with functional localization to an EGSCS to construct such an adaptive system. In our proposal, the switching GNP can switch the functionally localized GNPs (assigning GNPs) based on the special traffic. Simulation confirmed the adaptability and effectiveness of our proposal in daily office-building traffic.

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