Double-deck elevator group supervisory control system using genetic network programming with ant colony optimization

被引：0

作者：

Yu, Lu ^{[1
]}

Zhou, Jin ^{[1
]}

Mabu, Shingo ^{[1
]}

Hirasawa, Kotaro ^{[1
]}

Hu, Jinglu ^{[1
]}

Markon, Sandor ^{[2
]}

机构：

[1] Waseda Univ, Grad Sch Informat Prod & Syst, Wakamatsu Ku, Hibikino 2-7, Kitakyushu, Fukuoka, Japan

[2] Fujitec Co Ltd, Prod Dev HQ, Shiga, Japan

来源：

2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS | 2007年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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). 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.

引用

页码：1015 / +

页数：2