Variation of ant colony optimization parameters for solving the travelling salesman problem

Cheong, Pui Yue; Aggarwal, Deepa; Hanne, Thomas; Dornberger, Rolf

doi:10.1109/iscmi.2017.8279598

Cited by 9 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CVRP also refers to the process of designing the best and the shortest possible route that connects one location to other geographically distributed locations, for example, school, warehouse, store, university, customer, city, etc. [22][23][24].…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Ant Colony System Algorithm Based on Subpaths for Solving the Capacitated Vehicle Routing Problem

Ahmed,

Hameed,

Mutar

et al. 2023

Symmetry

View full text Add to dashboard Cite

The capacitated vehicle routing problem (CVRP) is regarded as an NP-hard problem. Moreover, the CVRP is described as a model that can be used in many applications such as transport, logistics, and distribution. The exact algorithms can find exact optimal solutions on the small-sized problem instances; however, for large-sized instances it is difficult to find the exact optimal solutions in polynomial time. This reason motivated the researchers to present heuristic/metaheuristic algorithms to solve large-sized problem instances within a reasonable computational time. One of the good algorithms that deal with the CVRP is the ant colony optimization (ACO) algorithm. Several ACO algorithms have been suggested in the literature, such as the ant system (AS) algorithm, ant colony system (ACS) algorithm, and so on. On the other hand, ACO is designed to solve the path problem that finds the best way. However, this algorithm still lacks exploratory mechanisms, which results in premature convergence and stagnation issues. Therefore, we propose to develop an enhanced ACS (EACS) algorithm for solving the CVRP based on subpaths. In our proposed algorithm, we propose to utilize the K-nearest neighbour (KNN) algorithm for finding the best initial solution and then enhance the diversity mechanism of the proposed algorithm by avoiding the generation of the same solution using subpaths. This uses the diversity of the generated solution to find a better solution with a shorter route in a reasonable amount of computational time. Furthermore, we propose to apply the three-opt algorithm to the completed subtour and the k-opt algorithm to the subpath gained from the experience of the subpath. Finally, to verify the effectiveness of the proposed EACS algorithm, the algorithm is tested on some CVRP instances and is compared with one of the state-of-the-art methods, namely, the enhanced simulated annealing algorithm. The comparative study showed a better performance of our EACS compared to the enhanced simulated annealing algorithm.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Ant Colony System Algorithm Based on Subpaths for Solving the Capacitated Vehicle Routing Problem

Ahmed,

Hameed,

Mutar

et al. 2023

Symmetry

View full text Add to dashboard Cite

show abstract

“…ACO algorithms have achieved satisfactory feedback both in terms of temporal complexity and search efficiency compared to heuristic methods [12]. This algorithm is considered to be a distributed system in which ants work in parallel to provide positive feedback and has been applied to find a solution to problems related to transportation [13] and combinatorial optimization and communication networks [14], [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Towards a Distributed SMA-based Solution for the Interoperability of Hospital Information Systems for Better Routing of Emergency Ambulances

et al. 2021

View full text Add to dashboard Cite

Multi-agent systems MASs have been widely used to interoperate hospital information systems (HISs). The use of MASs for HISs interoperability has become a central solution, especially in the field of emergency medicine. In emergencies, the notion of delay is relative, because responders only have a few minutes to react. This emergency response time has an important role in the event that an accident occurs on the road. Existing procedures for the emergency ambulance (EA) dispatch strategy are based on manual dispatch. In this work, we are introducing a distributed emergency ambulance (DEA) routing system to control emergency latency time, which includes driving route planning to guide emergency vehicles and the allocation of distributed emergency resources (emergency ambulances and hospitals) to reduce the EA response time caused by traffic or the wrong human decision to transport ambulance to the accident site. The allocation of resources (hospitals) is ensured through a recommendation system based on the interoperability of several interconnected HISs using a multi-agent system. The proposed solution takes into consideration dynamic traffic flow information during the day to build dynamic paths for EA. The improved method is based on a distributed architecture to calculate and find the optimal pathway for a set of emergency vehicles based on ACO ant colony optimization techniques. The results of the simulation show that the proposed method can decrease the total travel time of the ambulance to reach the accident position compared to conventional methods that use lights and sirens to warn other vehicles to free up the road for the ambulance or use a traditional approach based on the vision/reflection of the driver to choose in a random way the paths to take. Based on such a solution, ambulance staff will be able to save lives by optimizing the total journey with the minimum travel.

show abstract

“…They work in parallel to solve complex problems using optimal solutions. The system is based on two main components: (i) The VANET technology, for preparing the infrastructure of the road network (which uses several entities that guarantee the best exchange of information between vehicles and road controller agents) and (ii) The Ant Colony Optimization (ACO) (Tomera, 2014;Cheong et al, 2017), which is used to compute the shortest path based on the traffic data collected from the road network. The aforementioned traffic information is collected from several sensors implemented at each intersection, which are able to define the traffic flow on those particular routes.…”

Section: Introductionmentioning

confidence: 99%

Solving Traffic Routing System using VANet Strategy Combined with a Distributed Swarm Intelligence Optimization

Elgarej¹,

Mansouri²,

Youssfi³

2018

Journal of Computer Science

View full text Add to dashboard Cite

Proposing an efficient strategy to reduce traffic congestion is an essential step towards improvement as we take into consideration the unpredictable and dynamic infrastructure of the road network. With the advances in computing technologies and communications protocols, we can retrieve any type of data and receive in real-time the state of traffic congestion at each road using Electronic Toll Collection System (ETCS), Vehicle Traffic Routing System (VTRS), Intelligent Transportation System (ITS) and Traffic Light Signals (TLS). This study introduces a new distributed strategy that aims to optimize traffic road congestion in realtime based on the Vehicular Ad-Hoc Network (VANET) communication system and the techniques of the Ant Colony Optimization (ACO). The VANET is used as a communication technology that will help us create a channel of communication between several vehicles and routes. The techniques of the ACO is used to compute the shortest path that can be followed by the driver to avoid congested routes. The proposed system is based on a multi-agent architecture, in which all agents work together to monitor the road traffic congestion and help drivers quickly arrive at their destinations by following the best routes with less congestion. Simulation results show that the proposed method can reduce the total distance traveled and time taken in order to reach a destination, as compared to the classic "shortest path method" (based only on the distance).

show abstract

Variation of ant colony optimization parameters for solving the travelling salesman problem

Cited by 9 publications

References 11 publications

An Enhanced Ant Colony System Algorithm Based on Subpaths for Solving the Capacitated Vehicle Routing Problem

An Enhanced Ant Colony System Algorithm Based on Subpaths for Solving the Capacitated Vehicle Routing Problem

Towards a Distributed SMA-based Solution for the Interoperability of Hospital Information Systems for Better Routing of Emergency Ambulances

Solving Traffic Routing System using VANet Strategy Combined with a Distributed Swarm Intelligence Optimization

Contact Info

Product

Resources

About