Coordinated Ramp Metering Based on Ant Colony Optimization

2011 ◽  
Vol 97-98 ◽  
pp. 990-993
Author(s):  
Xin Rong Liang ◽  
Jian Xiong
Keyword(s):  
Control System ◽  
Ant Colony Optimization ◽  
Traffic Flow ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Macroscopic Model ◽  
Traffic Density ◽  
Model Parameters ◽  
Ramp Control ◽  
Control Layer

Hierarchy control strategy and ant colony optimization are proposed for coordinated ramp control. The macroscopic model to describe the evolution of freeway traffic flow is firstly established. Then the coordinated ramp control system is designed. There are two layers in this coordinated control system: the coordination control layer to select traffic models, to adjust the model parameters, and to determine the desired traffic density in each freeway section according to the current traffic status; and the direct control layer to keep the actual values of state variables in the vicinity of the desired state points via PI controllers. Ant colony algorithm is used to find the optimal PI control parameters. Simulation results show that the control system is of good performance. It can eliminate traffic jams, maintain traffic flow stability, and make vehicles travel more efficiently and safely.

Intelligent Control System on Traffic Flow under the Keep-Right-Except-to-Pass Rule

2014 ◽  
Vol 951 ◽  
pp. 19-24
Author(s):  
Yi Hui Zha
Keyword(s):  
Control System ◽  
Traffic Flow ◽  
Intelligent Control ◽  
Evaluation Model ◽  
Flow Distribution ◽  
Flow Equation ◽  
Traffic Density ◽  
Model Parameters ◽  
Intelligent Control System ◽  
Flow Through

This paper studied the traffic flow under the keep-right-except-to-pass rule on freeways and the effect of intelligent control system on traffic flow. Through the analysis of the basic traffic flow equation, we developed the traffic flow-traffic density model, obtained the maximum traffic flow, determined the model parameters and tested the model. We defined an indicator reflecting of the degree of traffic flow distribution according to the Gini coefficient in economics and built an effect evaluation model to study the effect of intelligent control system on traffic flow.

Research on Application of Ant Colony Optimization

2014 ◽  
Vol 548-549 ◽  
pp. 1213-1216
Author(s):  
Wang Rui ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Algorithm ◽  
Convergence Speed ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Slow Convergence ◽  
Simulation Results ◽  
Research On Application

We research on application of ant colony optimization. In order to avoid the stagnation and slow convergence speed of ant colony algorithm, this paper propose the multiple ant colony optimization algorithm based on the equilibrium of distribution. The simulation results show that the optimal algorithm can have better balance in reducing stagnation and improving the convergence.

Application of Ant Colony Algorithm

2014 ◽  
Vol 548-549 ◽  
pp. 1217-1220
Author(s):  
Rui Wang ◽  
Zai Tang Wang
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Pheromone Concentration ◽  
Local Optimal Solution ◽  
Searching Ability ◽  
Excessive Accumulation

This paper mainly considers the application of the ant colony in our life. The principle of ant colony optimization, improves the performance of ant colony algorithm, and the global searching ability of the algorithm. We introduce a new adaptive factor in order to avoid falling into local optimal solution. With the increase the number of interations, this factor will benefit the ant search the edge with lower pheromone concentration and avoid the excessive accumulation of pheromone.

Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining

2012 ◽  
Vol 433-440 ◽  
pp. 3577-3583
Author(s):  
Yan Zhang ◽  
Hao Wang ◽  
Yong Hua Zhang ◽  
Yun Chen ◽  
Xu Li
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Path ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Computing Power ◽  
Slow Convergence ◽  
The Impact

To overcome the defect of the classical ant colony algorithm’s slow convergence speed, and its vulnerability to local optimization, the authors propose Parallel Ant Colony Optimization Algorithm Based on Multiplicate Pheromon Declining to solve Traveling Salesman Problem according to the characteristics of natural ant colony multi-group and pheromone updating features of ant colony algorithm, combined with OpenMP parallel programming idea. The new algorithm combines three different pheromone updating methods to make a new declining pheromone updating method. It effectively reduces the impact of pheromone on the non-optimal path in the ants parade loop to subsequent ants and improves the parade quality of subsequent ants. It makes full use of multi-core CPU's computing power and improves the efficiency significantly. The new algorithm is compared with ACO through experiments. The results show that the new algorithm has faster convergence rate and better ability of global optimization than ACO.

A flexibility method for structural damage identification using continuous ant colony optimization

2015 ◽  
Vol 11 (2) ◽  
pp. 186-201 ◽  
Author(s):  
Maryam Daei ◽  
S. Hamid Mirmohammadi
Keyword(s):  
Experimental Data ◽  
Damage Detection ◽  
Ant Colony Optimization ◽  
Structural Damage ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Computational Time ◽  
Content Type ◽  
Flexibility Matrix

Purpose – The interest in the ability to detect damage at the earliest possible stage is pervasive throughout the civil engineering over the last two decades. In general, the experimental techniques for damage detection are expensive and require that the vicinity of the damage is known and readily accessible; therefore several methods intend to detect damage based on numerical model and by means of minimum experimental data about dynamic properties or response of damaged structures. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the damage detection problem is formulated as an optimization problem such as to obtain the minimum difference between the numerical and experimental variables, and then a modified ant colony optimization (ACO) algorithm is proposed for solving this optimization problem. In the proposed algorithm, the structural damage is detected by using dynamically measured flexibility matrix, since the flexibility matrix of the structure can be estimated from only the first few modes. The continuous version of ACO is employed as a probabilistic technique for solving this computational problem. Findings – Compared to classical methods, one of the main strengths of this meta-heuristic method is the generally better robustness in achieving global optimum. The efficiency of the proposed algorithm is illustrated by numerical examples. The proposed method enables the deduction of the extent and location of structural damage, while using short computational time and resulting good accuracy. Originality/value – Finding accurate results by means of minimum experimental data, while using short computational time is the final goal of all researches in the structural damage detection methods. In this paper, it gains by applying flexibility matrix in the definition of objective function, and also via using continuous ant colony algorithm as a powerful meta-heuristic techniques in the constrained nonlinear optimization problem.

scholarly journals Low-Energy Secure Routing Protocol for WSNs Based on Multiobjective Ant Colony Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xueli Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Ant Colony Optimization Algorithm

As one of the three pillars of information technology, wireless sensor networks (WSNs) have been widely used in environmental detection, healthcare, military surveillance, industrial data sampling, and many other fields due to their unparalleled advantages in deployment cost, network power consumption, and versatility. The advent of the 5G standard and the era of Industry 4.0 have brought new opportunities for the development of wireless sensor networks. However, due to the limited power capacity of the sensor nodes themselves, the harsh deployment environment will bring a great difficulty to the energy replenishment of the sensor nodes, so the energy limitation problem has become a major factor limiting its further development; how to improve the energy utilization efficiency of WSNs has become an urgent problem in the scientific and industrial communities. Based on this, this paper researches the routing technology of wireless sensor networks, from the perspective of improving network security, and reducing network energy consumption, based on the study of ant colony optimization algorithm, further studies the node trust evaluation mechanism, and carries out the following research work: (1) study the energy consumption model of wireless sensor networks; (2) basic ant colony algorithm improvement; (3) multiobjective ant colony algorithm based on wireless sensor routing algorithm optimization. In this study, the NS2 network simulator is used as a simulation tool to verify the performance of the research algorithm. Compared with existing routing algorithms, the simulation results show that the multiobjective ant colony optimization algorithm has better performance in evaluation indexes such as life cycle, node energy consumption, node survival time, and stability compared with the traditional algorithm and the dual cluster head ant colony optimization algorithm.

scholarly journals Multirobot searching method of natural gas leakage sources on offshore platform using ant colony optimization

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142095901
Author(s):  
Tao Ma ◽  
Shuhai Liu ◽  
Huaping Xiao
Keyword(s):  
Natural Gas ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Offshore Platform ◽  
Ant Colony ◽  
Detection Methods ◽  
Offshore Platforms ◽  
Gas Leakage ◽  
Source Positioning

Natural gas leakage on offshore platforms has a great impact on safety production, and effective and reasonable leakage detection methods can prevent the harm caused by natural gas leakage. This article proposes a method based on ant colony optimization (ACO) for multirobots to collaboratively search for leaking natural gas sources on offshore platforms. First, analyze the structure and environment of the offshore platform, use Fluent software to simulate the diffusion process of natural gas leaked from the platform, and establish a diffusion model of natural gas leaked from various aspects, such as the layout of different platforms, the number of leaked gas sources, and the concentration of leaked gas sources. In terms of multirobot cooperative control, we analyzed and improved the ant colony algorithm and proposed a multirobot cooperative search strategy for gas search, gas tracking, and gas source positioning. The multirobot search process was simulated using MATLAB software, and the robot on the detection effect of multirobots was analyzed in many aspects, such as quantity, location of leak source, and a number of leak sources, which verified the feasibility and effectiveness of the multirobot control strategy based on optimized ACO. Finally, we analyze and compare the two control algorithms based on ACO and cuckoo search algorithm (CSA). The results show that the ACO-based multirobot air source positioning effect is significantly better than CSA.

Application of Emergency Logistics Distribution Routing Optimization Based on Improved Ant Colony Algorithm

2011 ◽  
Vol 268-270 ◽  
pp. 1726-1732 ◽  
Author(s):  
Li Yi Zhang ◽  
Teng Fei ◽  
Jin Zhang ◽  
Jie Li
Keyword(s):  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Delivery Time ◽  
Emergency Relief ◽  
Logistics Distribution ◽  
The Road ◽  
Routing Optimization ◽  
Emergency Logistics ◽  
Improved Ant Colony Algorithm

Emergency relief has characteristics of complexity, urgency, sustainability, technicality, and so on. In this paper a mathematical model to seek the shortest delivery time as the ultimate goal is established based on these characteristics, which is on the core of characteristics with the urgency and consider both the road conditions and on shortage of demand point of relief supplies. The problem of emergency logistics distribution routing optimization is solved by the improved ant colony algorithm—Fish-Swarm Ant Colony Optimization (FSACO), simulation results show that, compared with basic ant colony algorithm, Fish-Swarm Ant Colony Optimization can find the higher quality to solve the problem of emergency logistics distribution routing optimization.

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.

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