Bee colony double inhibition labor division algorithm and its application in traffic signal timing

doi:10.11772/j.issn.1001-9081.2018112337

Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (7): 1899-1904.DOI: 10.11772/j.issn.1001-9081.2018112337

• Artificial intelligence • Previous Articles Next Articles

Bee colony double inhibition labor division algorithm and its application in traffic signal timing

HU Liang¹, XIAO Renbin¹, LI Hao²

1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan Hubei 430074, China;
2. Department of Early Warning Information, Air Force Early Warning Academy, Wuhan Hubei 430019, China

Received:2018-11-27 Revised:2019-01-13 Online:2019-07-15 Published:2019-07-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (51875220).

蜂群双抑制劳动分工算法及其在交通信号配时中的应用

胡亮¹, 肖人彬¹, 李浩²

1. 华中科技大学人工智能与自动化学院, 武汉 430074;
2. 空军预警学院预警情报系, 武汉 430019

通讯作者: 肖人彬
作者简介:胡亮(1994-),男,湖北咸宁人,硕士研究生,主要研究方向:群智能;肖人彬(1965-),男,湖北武汉人,教授,博士,主要研究方向:复杂系统建模与仿真、群智能、涌现计算;李浩(1981-),男,湖北孝感人,讲师,博士,主要研究方向:群智能、无人机集群、指挥自动化、信号处理。
基金资助:
国家自然科学基金资助项目（51875220）。

Abstract

Abstract:

Swarm intelligence labor division refers to any algorithm and distributed problem solving method that is inspired by the collective behaviors of social insects and other animal groups. It can be widely used in real-life task assignment. Focusing on the task assignment problem like traffic signal timing, the theory of labor division that describes the interaction mode between bee individuals was introduced, a Bee colony Double Inhibition Labor Division Algorithm (BDILDA) based on swarm intelligence was proposed, in which the dynamic accommodation of swarm labor division was achieved through interaction between internal and external inhibitors of the individual. In order to verify the validity of BDILDA, the traffic signal timing problem was selected for simulation experiments. BDILDA was used to solve actual case of traffic signal timing and the result was compared with the results of Webster algorithm, Multi-Colony Ant Algorithm (MCAA), Transfer Bees Optimizer (TBO) algorithm and Backward FireWorks Algorithm (BFWA). The experimental results show that average delay time of BDILDA is reduced by 14.3-20.1 percentage points, the average parking times is reduced by 3.7-4.5 percentage points, the maximum traffic capacity is increased by 5.2-23.6 percentage points. The results indicate that the proposed algorithm is suitable for solving dynamic assignment problems in uncertain environment.

Key words: swarm intelligence, labor division, bee colony double inhibition principle, traffic signal timing

摘要：

群智能劳动分工是指任何启发于群居性昆虫和其他动物群体的集体行为而设计的算法和分布式问题解决方式，可以广泛用于现实生活中的任务分配问题。针对交通信号配时这类任务分配问题，引入描述蜜蜂个体之间交互方式的劳动分工理论，提出了一种基于群智能的蜂群双抑制劳动分工算法（BDILDA），该算法通过个体内部抑制剂和外部抑制剂的相互作用，达到群体劳动分工的动态调节。为了验证BDILDA的有效性，选取交通信号配时问题进行仿真实验。采用BDILDA对实际案例进行了交通信号配时求解，并把所得结果与Webster算法、群智能多种群蚁群算法（MCAA）、迁移蜂群（TBO）算法和反向烟花算法（BFWA）得出的结果进行了对比。实验结果显示所提算法减小平均延误时间14.3~20.1个百分点，减少平均停车次数3.7~4.5个百分点，在最大通行能力方面增加5.2~23.6个百分点。结果表明该算法适于求解不确定环境下的动态分配问题。

关键词: 群智能, 劳动分工, 蜂群双抑制原理, 交通信号配时

CLC Number:

TP18

HU Liang, XIAO Renbin, LI Hao. Bee colony double inhibition labor division algorithm and its application in traffic signal timing[J]. Journal of Computer Applications, 2019, 39(7): 1899-1904.

胡亮, 肖人彬, 李浩. 蜂群双抑制劳动分工算法及其在交通信号配时中的应用[J]. 计算机应用, 2019, 39(7): 1899-1904.

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Bee colony double inhibition labor division algorithm and its application in traffic signal timing

蜂群双抑制劳动分工算法及其在交通信号配时中的应用

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