A Multi-agent Ant Colony Optimization Algorithm for Effective Vehicular Traffic Management

S, Saju Sankar; S, Vinod Chandra S

doi:10.1007/978-3-030-53956-6_59

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…The PSO algorithm is used to optimize ACO parameters for better performance. With the help of intelligent sensors, SS et al (2020) proposed an ACO meta-heuristicbased model to control and regulate the traffic in transportation networks while minimizing travel time and congestion levels. The authors demonstrate that, due to its dynamic nature, the ACO algorithm is suitable to manage high traffic in big cities.…”

Section: Related Workmentioning

confidence: 99%

“…In the context of an urban road network, ACO can be used to calculate the optimal routes between different locations and can lead to efficient and effective solutions that take into account various factors such as distance, traffic flow and other constraints for the following reasons: One of the main advantages of ACO is that it can handle complex networks with multiple paths and variable conditions because it is a constructive algorithm. By using a pheromone-based approach, the algorithm can adapt to changing traffic conditions and find alternative routes if necessary (SS et al ., 2020). Additionally, ACO is a scalable algorithm that can be applied to networks of varying sizes, making it well-suited for urban road networks that can be quite large and complex (Di Caprio et al ., 2022).…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, our decision to use the ACO meta-heuristic for solving the traffic routing problem is supported by experimental results and studies from other researchers in the field. For instance, authors (Bedi et al ., 2007; Di Caprio et al ., 2022; SS et al ., 2020; Jiao et al ., 2018), recommended ACO as the most suitable algorithm to fix traffic routing problems, like the Traveling Salesman Problem (TSP) (Zukhri and Paputungan, 2013; Le and Peechatt, 2019), Vehicle Routing Problem VRP (Bell and McMullen, 2004; Jiang et al ., 2021), path planning and avoiding obstacles for automated vehicles (Wang et al ., 2019), for robots (Liu et al ., 2017; Cong and Ponnambalam, 2009), for Unmanned Aerial Vehicles UAV (Duan et al ., 2009; Chen et al ., 2022), for Autonomous Underwater Vehicle AUV (Mirjalili et al ., 2020) and for submarines (Fu et al ., 2022; Ma et al ., 2020).…”

Section: Related Workmentioning

confidence: 99%

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Urban traffic flow management on large scale using an improved ACO for a road transportation system

Boubedra,

Tolba,

Manzoni

et al. 2023

IJICC

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PurposeWith the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.Design/methodology/approachAn improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.FindingsExperimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.Originality/valueThe proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Urban traffic flow management on large scale using an improved ACO for a road transportation system

Boubedra,

Tolba,

Manzoni

et al. 2023

IJICC

View full text Add to dashboard Cite

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“…All the selected intersection measures of effectiveness (MOEs), including vehicle delay, parking rate, and the number of stops, were improved by a fair margin. Sankar and Chandra proposed a multi-agent ACO for effective traffic management on a network level [69]. The authors concluded that the method could be pretty useful in reducing average vehicle delays and traffic congestion under varying traffic conditions.…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Search Algorithm - Essence of Optimization

2023

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A pioneering researcher in soft computing and embedded systems, he holds dual doctorates in Computer Science & Engineering and Business Management and has two registered patents to his name. His primary research interests are in computer artificial intelligence, big data, analytics, embedded systems, and e-commerce. He has supervised around eighteen master's degree and twenty-four bachelor's degree students. He has published forty-eight papers in refereed journals and published six books with international publishers. He is the principal investigator in establishing a center of excellence for renowned technological giants like IBM, Oracle, Texas Instruments, and Huawei at PRMCEAM. He is instrumental in setting up industry-funded laboratories at ARM, Cypress Semiconductor Corporation, Intel FPGA, Wind River, and Xilinx. Dr. Harkut is a fellow of the Institute of Electronics and Telecommunication Engineers (IETE), a life member of the Indian Society for Technical Education (ISTE), a senior member of the Universal Association of Computer and Electronics Engineers (UACEE), and a professional member of the International Association of Engineers (IAENG). Contents Preface XIII Chapter 1 1 Introductory Chapter: Search Algorithm -Essence of Optimization by Dinesh G. Harkut Chapter 2 9 Search Algorithms on Logistic and Manufacturing Problems

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“…An improved hybrid ACO method was proposed (8,9) for decreasing the congestion in the network in smart cities. This method is also helpful in improving the quality of service during packet transmission.…”

Section: Ant Colony Optimizationmentioning

confidence: 99%

Implementation of ACO in Vanet with Detection of Faulty Node

Faisal¹,

Zaidi²

2021

IJST

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Objective: To propose an Ant Colony Optimization (ACO) based routing approach for estimating the reliability parameter of backward and forward ants. Methods: The ACO-based proposed methodology is used to predict path or route discovery in VANET. This study has discovered the best optimal path for data transmission. The forward ant movement has a minimum hop count of four for vehicle number 6. The vehicle 6 is a source vehicle, and it starts data transmission. The pheromone concentration of the entire vehicle defers and, after updating, the performance was analyzed. The best path is selected based on hop count, then a path with maximum pheromone concentration is selected. The pheromone evaporation factor is updated after a certain time interval. Findings: We have checked the reliability of the path, considering pheromone evaporation as a factor, the pheromone table is updated at a regular time interval, and the reliability parameter is also proposed for performing a task without failure in an efficient manner. We have arranged a routing table in descending order of hop count and then select the best of three paths with a minimum hop count. We search for hop count less than or equal to 5 and total pheromone greater than or equal to 17.55. We find that Ant 3 having a hop count of 5 and total pheromone concentration of 19.37. In this work, we have taken the evaporation factor as 0.5. If the transmission of a message is successful, then the reliability assigned to the edge will be 100, and for unsuccessful transmission, reliability is taken down by 20. If the reliability of the path will become 40, then the vehicle has to search for a new path. In this way, we eliminate malicious nodes for efficient transmission of message. Novelty: The performance of VANET is improved using ACO based proposed routing approach. The optimal path is selected with a minimum hop count and reliability parameter to identify the successful and unsuccessful path.

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A Multi-agent Ant Colony Optimization Algorithm for Effective Vehicular Traffic Management

Cited by 9 publications

References 9 publications

Urban traffic flow management on large scale using an improved ACO for a road transportation system

Urban traffic flow management on large scale using an improved ACO for a road transportation system

Search Algorithm - Essence of Optimization

Implementation of ACO in Vanet with Detection of Faulty Node

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