This paper deals with the optimization of the capacity of a terminal railway station using the Ant Colony Optimization algorithm. The capacity of the terminal station is defined as the
number of trains that depart from the station in unit interval of time. The railway capacity
optimization problem is framed as a typical symmetrical Travelling Salesman Problem (TSP),
with the TSP nodes representing the train arrival /departure events and the TSP total cost
representing the total time-interval of the schedule. The application problem is then optimized
using the ACO algorithm. The simulation experiments validate the formulation of the railway
capacity problem as a TSP and the ACO algorithm produces optimal solutions superior to those
produced by the domain experts.
The railway capacity optimization problem deals with the maximization of the number of trains running on
a given network per unit time. In this study, we frame this problem as a typical asymmetrical Travelling
Salesman Problem (ATSP), with the ATSP nodes representing the train arrival /departure events and the
ATSP total cost representing the total time-interval of the schedule. The application problem is then
optimized using the standard Ant Colony Optimization (ACO) algorithm. The simulation experiments
validate the formulation of the railway capacity problem as an ATSP and the ACO algorithm produces
optimal solutions superior to those produced by the domain experts.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Presentation of GreenYourMove's hybrid approach in the 3rd Conference on Sust...GreenYourMove
The document summarizes a hybrid approach to solving the environmental multi-modal journey planning problem. It uses Dijkstra's algorithm to find the closest public transportation nodes to the starting and ending points, and then builds a mixed integer linear program (MILP) to compute the optimal journey between those nodes that minimizes both travel time and environmental costs. The proposed method provides a novel way to address the multi-criteria optimization challenge of journey planning across multiple transportation modes.
Platforming_Automated_And_Quickly_BeamerPeter Sels
The document describes a model and software for automatically planning platform and route assignments for trains in busy railway stations. The model aims to minimize conflicts and optimize assignments using integer programming. The software was tested on stations in Antwerp and Ghent, showing it can find assignments with no conflicts faster than current manual methods and indicate robustness issues. Future work includes further integrating the approach with railway planners to avoid robustness issues and add more complexity to the model.
A COMPARISON BETWEEN SWARM INTELLIGENCE ALGORITHMS FOR ROUTING PROBLEMSecij
Travelling salesman problem (TSP) is a most popular combinatorial routing problem, belongs to the class of NP-hard problems. Many approacheshave been proposed for TSP.Among them, swarm intelligence (SI) algorithms can effectively achieve optimal tours with the minimum lengths and attempt to avoid trapping in local minima points. The transcendence of each SI is depended on the nature of the problem. In our studies, there has been yet no any article, which had compared the performance of SI algorithms for TSP perfectly. In this paper,four common SI algorithms are used to solve TSP, in order to compare the performance of SI algorithms for the TSP problem. These algorithms include genetic algorithm, particle swarm optimization, ant colony optimization, and artificial bee colony. For each SI, the various parameters and operators were tested, and the best values were selected for it. Experiments oversome benchmarks fromTSPLIBshow that
artificial bee colony algorithm is the best one among the fourSI-basedmethods to solverouting problems like TSP.
This document presents a study on developing an artificial intelligence system to manage real-time traffic. The study developed a traffic simulator using Python to model vehicle and traffic light behavior at an intersection. A linear regression model was then used to control the traffic lights dynamically based on current traffic conditions, collected from sensors. Testing showed the AI-based dynamic system improved traffic flow compared to a static traffic light system, allowing more vehicles to pass through the intersection in a given time period. The authors conclude the linear regression model provides better real-time traffic management than existing approaches and suggest further improving it with deep learning techniques.
2019-2020 research findings in Public Transit from the Centre for Transport Studies, University of TWENTE. The presented findings at the Transportation Research board include overcrowding, operational control, electric buses, and train assignment.
The railway capacity optimization problem deals with the maximization of the number of trains running on
a given network per unit time. In this study, we frame this problem as a typical asymmetrical Travelling
Salesman Problem (ATSP), with the ATSP nodes representing the train arrival /departure events and the
ATSP total cost representing the total time-interval of the schedule. The application problem is then
optimized using the standard Ant Colony Optimization (ACO) algorithm. The simulation experiments
validate the formulation of the railway capacity problem as an ATSP and the ACO algorithm produces
optimal solutions superior to those produced by the domain experts.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Presentation of GreenYourMove's hybrid approach in the 3rd Conference on Sust...GreenYourMove
The document summarizes a hybrid approach to solving the environmental multi-modal journey planning problem. It uses Dijkstra's algorithm to find the closest public transportation nodes to the starting and ending points, and then builds a mixed integer linear program (MILP) to compute the optimal journey between those nodes that minimizes both travel time and environmental costs. The proposed method provides a novel way to address the multi-criteria optimization challenge of journey planning across multiple transportation modes.
Platforming_Automated_And_Quickly_BeamerPeter Sels
The document describes a model and software for automatically planning platform and route assignments for trains in busy railway stations. The model aims to minimize conflicts and optimize assignments using integer programming. The software was tested on stations in Antwerp and Ghent, showing it can find assignments with no conflicts faster than current manual methods and indicate robustness issues. Future work includes further integrating the approach with railway planners to avoid robustness issues and add more complexity to the model.
A COMPARISON BETWEEN SWARM INTELLIGENCE ALGORITHMS FOR ROUTING PROBLEMSecij
Travelling salesman problem (TSP) is a most popular combinatorial routing problem, belongs to the class of NP-hard problems. Many approacheshave been proposed for TSP.Among them, swarm intelligence (SI) algorithms can effectively achieve optimal tours with the minimum lengths and attempt to avoid trapping in local minima points. The transcendence of each SI is depended on the nature of the problem. In our studies, there has been yet no any article, which had compared the performance of SI algorithms for TSP perfectly. In this paper,four common SI algorithms are used to solve TSP, in order to compare the performance of SI algorithms for the TSP problem. These algorithms include genetic algorithm, particle swarm optimization, ant colony optimization, and artificial bee colony. For each SI, the various parameters and operators were tested, and the best values were selected for it. Experiments oversome benchmarks fromTSPLIBshow that
artificial bee colony algorithm is the best one among the fourSI-basedmethods to solverouting problems like TSP.
This document presents a study on developing an artificial intelligence system to manage real-time traffic. The study developed a traffic simulator using Python to model vehicle and traffic light behavior at an intersection. A linear regression model was then used to control the traffic lights dynamically based on current traffic conditions, collected from sensors. Testing showed the AI-based dynamic system improved traffic flow compared to a static traffic light system, allowing more vehicles to pass through the intersection in a given time period. The authors conclude the linear regression model provides better real-time traffic management than existing approaches and suggest further improving it with deep learning techniques.
2019-2020 research findings in Public Transit from the Centre for Transport Studies, University of TWENTE. The presented findings at the Transportation Research board include overcrowding, operational control, electric buses, and train assignment.
This document describes a novel predator-prey pigeon-inspired optimization (PPPIO) algorithm to solve the problem of three-dimensional path planning for uninhabited combat aerial vehicles (UCAVs) in a dynamic environment. The PPPIO algorithm is proposed to minimize a cost function representing factors such as path length, altitude, danger zone avoidance, power consumption, collisions, and path smoothness. It introduces predator-prey concepts to the pigeon-inspired optimization algorithm to improve convergence and avoid local optima. Simulation results show the PPPIO performs better than other algorithms at solving the UCAV path planning problem.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
IRJET- Design and Development of Traffic Flow Prediction System for Efficient...IRJET Journal
This document discusses the design and development of a traffic flow prediction system for Amravati City, India to improve traffic movements. It begins by noting the increasing traffic problems in Amravati due to rising vehicle numbers. The city currently uses pre-timed traffic signal controls that are inefficient. The paper proposes an intelligent transportation system using traffic signal optimization and coordination to predict traffic flows. It reviews literature on traffic simulation software and signal timing optimization methods. It then describes the methodology for developing the prediction system, which involves data collection, network modeling, simulation calibration, and using VISSIM and Synchro software to simulate and optimize traffic flows. The goal is to reduce delays, queues and travel times at intersections in Amravati.
1907555 ant colony optimization for simulated dynamic multi-objective railway...Mamun Hasan
This document discusses using ant colony optimization algorithms to solve a simulated dynamic multi-objective railway junction rescheduling problem (DM-RJRP). It aims to identify features that enable ant colony algorithms to handle problems that are both dynamic and multi-objective. It proposes modifying two ant colony algorithms - population-based ant colony optimization (P-ACO) and MAX-MIN ant system (MMAS) - to generate a Pareto optimal set of trade-off solutions for the DM-RJRP. Multiple versions of the MMAS algorithm are designed that either retain or clear the pheromone values and non-dominated solutions after a dynamic change occurs.
The high increase in the number of traffic accidents involving rear-end collisions has caused significant damages, prompting the need for a more sensitive car following model that accurately depicts real-world traffic environments. This document reviews fuzzy microscopic traffic models, which use linguistic terms and rules rather than deterministic mathematical functions to describe driving behavior under car following conditions. Traditional car following models make unrealistic assumptions around symmetry, safe headways, and constant acceleration/deceleration. Fuzzy logic models treat drivers as decision-makers who determine controls based on sensory inputs evaluated through fuzzy reasoning. Input variables like relative velocity and distance divergence are evaluated using fuzzy functions and rules to estimate acceleration and deceleration rates.
Iaetsd modified artificial potential fields algorithm for mobile robot path ...Iaetsd Iaetsd
This document presents a modified artificial potential fields algorithm for mobile robot path planning in unknown and dynamic environments. The algorithm uses artificial potential fields to iteratively find optimal points to form a collision-free path from the start to destination. For static obstacles, potential values are used to identify clusters of points around the start and goal, and find a connecting midpoint. This process is repeated iteratively. For dynamic obstacles, Markov models are used to analyze obstacle behavior from sensor data and predict collision points. The robot's path is replanned as needed to avoid collisions based on feedback from sensors and odometry. Simulation results show the algorithm can efficiently plan paths in unknown environments and avoid both static and dynamic obstacles.
Conflict-free dynamic route multi-agv using dijkstra Floyd-warshall hybrid a...IJECEIAES
This document summarizes a research study that proposes a hybrid algorithm using Dijkstra and Floyd-Warshall algorithms to find conflict-free routes for multiple autonomous guided vehicles (AGVs) in a warehouse distribution system. The goal is to optimize the shortest distances and schedules for AGVs performing pickup and delivery tasks within specified time windows. A grid topology route model is used to represent the warehouse environment. The hybrid algorithm combines dynamic programming approaches to determine optimized, conflict-free routes for multiple AGVs operating simultaneously while meeting time constraints for tasks at workstations. The algorithm aims to improve efficiency of autonomous vehicle systems for goods distribution in manufacturing warehouse applications.
An Offline Hybrid IGP/MPLS Traffic Engineering Approach under LSP ConstraintsEM Legacy
This document proposes an offline hybrid IGP/MPLS traffic engineering approach to optimize network performance under LSP constraints. It presents models for hybrid routing including basic IGP shortcut and overlay models. A genetic algorithm heuristic is used to find an optimal set of LSPs to install while minimizing maximum link utilization. The approach is evaluated on three networks and shown to improve upon traditional IGP routing while approaching the performance of optimal MPLS solutions.
This summary provides the key details about a research paper on power delay profile estimation for MIMO-OFDM systems in 3 sentences:
The paper proposes a new technique to estimate the power delay profile for linear minimum mean square error channel estimation in MIMO-OFDM systems using only pilot symbols from all transmit antennas. The technique aims to mitigate distortions caused by null subcarriers and insufficient channel impulse response samples for estimation. Simulation results showed that channel estimation using the proposed power delay profile approach performed similarly to Wiener filtering by reducing distortion effects.
Cooperative Traffic Control based on the Artificial Bee Colony IJERA Editor
This paper studies the traffic control problem in an isolated intersection without traffic lights and phase, because the right-of-way is distributed to each vehicle individually based on connection of the Vehicle-to-Infrastructure (V2I), and the compatible streams are dynamically combined according to the arrival vehicles in each traffic flows. The control objective in the proposed algorithm is to minimize the time delay, which is defined as the difference between the travel time in real state and that in free flow state. In order to realize this target, a cooperative control structure with a two-way communications is proposed. First of all, once the vehicle enters the communication zone, it sends its information to the intersection. Then the passing sequence is optimized in the intersection with the heuristic algorithm of the Artificial Bee Colony, based on the arrival interval of the vehicles. At last, each vehicle plans its speed profile to meet the received passing sequence by V2I. The simulation results show that each vehicle can finish the entire travel trip with a near free flow speed in the proposed method.
In the present scenario, research conducted is mostly based on determining the duration of green light.
Moreover the research papers published on Adaptive Traffic Management did not focus much on the
concept of handling Emergency Vehicles. This major role of this project is as a continuation to the
existing research papers published on this topic. Here we not only handle traffic effectively but also
elaborate on effective management of highly prioritized vehicles through all possible phases. In this
particular research paper, Wireless Sensor Networks (WSN) is assumed to be the source of input.
The document summarizes an optimization program that airlines can use to determine the right freight capacity, operating frequency, and fleet positioning to minimize costs and maximize profits. The program takes in data on routes, yields, demands, and costs. It then runs integer programming models and U-curve techniques to find the optimum solution. A case study on Yemenia airline shows how the program can determine the best aircraft types for its network and maximize profits on a multi-stop route from Sana'a to Singapore.
Enhancing three variants of harmony search algorithm for continuous optimizat...IJECEIAES
Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has a slow convergence rate, which causes the algorithm to have a weak exploitation process in finding the global optima. Different variants of HS introduced in the literature to enhance the algorithm and fix its problems, but in most cases, the algorithm still has a slow convergence rate. Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the performance of different optimization algorithms, including HS. In this work, we adopted a new improved version of OBL, to improve three variants of Harmony Search, by increasing the convergence rate speed of these variants and improving overall performance. The new OBL version named improved opposition-based learning (IOBL), and it is different from the original OBL by adopting randomness to increase the solution's diversity. To evaluate the hybrid algorithms, we run it on benchmark functions to compare the obtained results with its original versions. The obtained results show that the new hybrid algorithms more efficient compared to the original versions of HS. A convergence rate graph is also used to show the overall performance of the new algorithms.
Car Dynamics using Quarter Model and Passive Suspension, Part II: A Novel Sim...IOSR Journals
This document presents research on using a quarter-car model and passive suspension to analyze the dynamics of a vehicle crossing a novel simple harmonic speed hump. The study uses MATLAB simulation of a quarter-car model to investigate the effect of hump dimensions and vehicle speed on ride comfort. It is found that a simple harmonic hump of 9m length allows vehicle speeds up to 30 km/h while maintaining ride comfort. A diagram is presented showing maximum vehicle speed for different hump dimensions that meet ride comfort standards.
This document discusses intelligent traffic light control using multi-agent reinforcement learning. It summarizes three research papers on the topic. The first paper proposes a distributed Q-learning approach that considers both motorized and non-motorized traffic to achieve near-global optimization. The second designs a two-stage negotiation system where traffic lights determine green times based on real-time traffic conditions. The third applies particle swarm optimization to find optimal light cycles for large vehicular networks under various scenarios.
1) The document summarizes a simulation of shuttle bus operations for the University of Cincinnati's North Route shuttle using Arena simulation software.
2) Three scenarios were modeled - a single large 26-seat bus, two large 26-seat buses, and two smaller 14-seat buses.
3) Based on the statistical analysis of 100 replications of each scenario, using two smaller 14-seat buses achieved the best utilization with the lowest average number of empty seats while maintaining a reasonable average passenger waiting time.
Vehicle route scheduling and transportation cost minimization in a latex indu...IJRES Journal
The vehicle route scheduling problem is concerned with the determination of routes and schedules for a fleet of vehicles to satisfy the demands of a set of customers. The goal of vehicle routing is to schedule multiple suppliers from various places. Vehicle routing has existed since the advent of the Industrial age, when large-scale production became possible. As the complexity and scale of the manufacturing world increased, the task of optimizing vehicle routing grew. The vehicle routing problem is a combinatorial optimization and integer programming problem seeking to service a number of customers with a fleet of vehicles. Often the context is that of delivering goods located at a central depot to customers who have placed orders for such goods or vice-versa. Implicit is the goal of minimizing the cost of distributing the goods. Many methods have been developed for searching for good solutions to the problem, however even for the smallest problems, finding global minimum for the cost function is computationally complex. The paper presents an optimization algorithm using Particle Swarm Optimization (PSO) for the vehicle routing that would enable the logistic manager of a latex industry to minimize the transportation cost and maximize the collection using minimum number of vehicles.
Solving real world delivery problem using improved max-min ant system with lo...ijaia
This paper presents a solution to real-world delive
ry problems (RWDPs) for home delivery services wher
e
a large number of roads exist in cities and the tra
ffic on the roads rapidly changes with time. The
methodology for finding the shortest-travel-time to
ur includes a hybrid meta-heuristic that combines a
nt
colony optimization (ACO) with Dijkstra’s algorithm
, a search technique that uses both real-time traff
ic
and predicted traffic, and a way to use a real-worl
d road map and measured traffic in Japan. We
previously proposed a hybrid ACO for RWDPs that use
d a MAX-MIN Ant System (MMAS) and proposed a
method to improve the search rate of MMAS. Since tr
affic on roads changes with time, the search rate i
s
important in RWDPs. In the current work, we combine
the hybrid ACO method with the improved MMAS.
Experimental results using a map of central Tokyo a
nd historical traffic data indicate that the propos
ed
method can find a better solution than conventional
methods.
This document proposes an improved hybrid behavior ant colony algorithm to solve vehicle routing problems. It defines four types of ant behaviors - random, greedy, pheromone-based, and a hybrid behavior considering factors like distance, saving value, and vehicle load. The algorithm allows ants to select behaviors and routes probabilistically based on these factors. Simulation experiments on a 31-city dataset show the hybrid behavior outperforms basic ant colony and other variants, finding better solutions on average. The results demonstrate this improved algorithm can effectively solve vehicle routing problems.
A STUDY AND IMPLEMENTATION OF THE TRANSIT ROUTE NETWORK DESIGN PROBLEM FOR A ...cscpconf
The design of public transportation networks presupposes solving optimization problems,
involving various parameters such as the proper mathematical description of networks, the
algorithmic approach to apply, and also the consideration of real-world, practical
characteristics such as the types of vehicles in the network, the frequencies of routes, demand,
possible limitations of route capacities, travel decisions made by passengers, the environmental
footprint of the system, the available bus technologies, besides others. The current paper
presents the progress of the work that aims to study the design of a municipal public
transportation system that employs middleware technologies and geographic information
services in order to produce practical, realistic results. The system employs novel optimization
approaches such as the particle swarm algorithms and also considers various environmental
parameters such as the use of electric vehicles and the emissions of conventional ones.
This document describes a novel predator-prey pigeon-inspired optimization (PPPIO) algorithm to solve the problem of three-dimensional path planning for uninhabited combat aerial vehicles (UCAVs) in a dynamic environment. The PPPIO algorithm is proposed to minimize a cost function representing factors such as path length, altitude, danger zone avoidance, power consumption, collisions, and path smoothness. It introduces predator-prey concepts to the pigeon-inspired optimization algorithm to improve convergence and avoid local optima. Simulation results show the PPPIO performs better than other algorithms at solving the UCAV path planning problem.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
IRJET- Design and Development of Traffic Flow Prediction System for Efficient...IRJET Journal
This document discusses the design and development of a traffic flow prediction system for Amravati City, India to improve traffic movements. It begins by noting the increasing traffic problems in Amravati due to rising vehicle numbers. The city currently uses pre-timed traffic signal controls that are inefficient. The paper proposes an intelligent transportation system using traffic signal optimization and coordination to predict traffic flows. It reviews literature on traffic simulation software and signal timing optimization methods. It then describes the methodology for developing the prediction system, which involves data collection, network modeling, simulation calibration, and using VISSIM and Synchro software to simulate and optimize traffic flows. The goal is to reduce delays, queues and travel times at intersections in Amravati.
1907555 ant colony optimization for simulated dynamic multi-objective railway...Mamun Hasan
This document discusses using ant colony optimization algorithms to solve a simulated dynamic multi-objective railway junction rescheduling problem (DM-RJRP). It aims to identify features that enable ant colony algorithms to handle problems that are both dynamic and multi-objective. It proposes modifying two ant colony algorithms - population-based ant colony optimization (P-ACO) and MAX-MIN ant system (MMAS) - to generate a Pareto optimal set of trade-off solutions for the DM-RJRP. Multiple versions of the MMAS algorithm are designed that either retain or clear the pheromone values and non-dominated solutions after a dynamic change occurs.
The high increase in the number of traffic accidents involving rear-end collisions has caused significant damages, prompting the need for a more sensitive car following model that accurately depicts real-world traffic environments. This document reviews fuzzy microscopic traffic models, which use linguistic terms and rules rather than deterministic mathematical functions to describe driving behavior under car following conditions. Traditional car following models make unrealistic assumptions around symmetry, safe headways, and constant acceleration/deceleration. Fuzzy logic models treat drivers as decision-makers who determine controls based on sensory inputs evaluated through fuzzy reasoning. Input variables like relative velocity and distance divergence are evaluated using fuzzy functions and rules to estimate acceleration and deceleration rates.
Iaetsd modified artificial potential fields algorithm for mobile robot path ...Iaetsd Iaetsd
This document presents a modified artificial potential fields algorithm for mobile robot path planning in unknown and dynamic environments. The algorithm uses artificial potential fields to iteratively find optimal points to form a collision-free path from the start to destination. For static obstacles, potential values are used to identify clusters of points around the start and goal, and find a connecting midpoint. This process is repeated iteratively. For dynamic obstacles, Markov models are used to analyze obstacle behavior from sensor data and predict collision points. The robot's path is replanned as needed to avoid collisions based on feedback from sensors and odometry. Simulation results show the algorithm can efficiently plan paths in unknown environments and avoid both static and dynamic obstacles.
Conflict-free dynamic route multi-agv using dijkstra Floyd-warshall hybrid a...IJECEIAES
This document summarizes a research study that proposes a hybrid algorithm using Dijkstra and Floyd-Warshall algorithms to find conflict-free routes for multiple autonomous guided vehicles (AGVs) in a warehouse distribution system. The goal is to optimize the shortest distances and schedules for AGVs performing pickup and delivery tasks within specified time windows. A grid topology route model is used to represent the warehouse environment. The hybrid algorithm combines dynamic programming approaches to determine optimized, conflict-free routes for multiple AGVs operating simultaneously while meeting time constraints for tasks at workstations. The algorithm aims to improve efficiency of autonomous vehicle systems for goods distribution in manufacturing warehouse applications.
An Offline Hybrid IGP/MPLS Traffic Engineering Approach under LSP ConstraintsEM Legacy
This document proposes an offline hybrid IGP/MPLS traffic engineering approach to optimize network performance under LSP constraints. It presents models for hybrid routing including basic IGP shortcut and overlay models. A genetic algorithm heuristic is used to find an optimal set of LSPs to install while minimizing maximum link utilization. The approach is evaluated on three networks and shown to improve upon traditional IGP routing while approaching the performance of optimal MPLS solutions.
This summary provides the key details about a research paper on power delay profile estimation for MIMO-OFDM systems in 3 sentences:
The paper proposes a new technique to estimate the power delay profile for linear minimum mean square error channel estimation in MIMO-OFDM systems using only pilot symbols from all transmit antennas. The technique aims to mitigate distortions caused by null subcarriers and insufficient channel impulse response samples for estimation. Simulation results showed that channel estimation using the proposed power delay profile approach performed similarly to Wiener filtering by reducing distortion effects.
Cooperative Traffic Control based on the Artificial Bee Colony IJERA Editor
This paper studies the traffic control problem in an isolated intersection without traffic lights and phase, because the right-of-way is distributed to each vehicle individually based on connection of the Vehicle-to-Infrastructure (V2I), and the compatible streams are dynamically combined according to the arrival vehicles in each traffic flows. The control objective in the proposed algorithm is to minimize the time delay, which is defined as the difference between the travel time in real state and that in free flow state. In order to realize this target, a cooperative control structure with a two-way communications is proposed. First of all, once the vehicle enters the communication zone, it sends its information to the intersection. Then the passing sequence is optimized in the intersection with the heuristic algorithm of the Artificial Bee Colony, based on the arrival interval of the vehicles. At last, each vehicle plans its speed profile to meet the received passing sequence by V2I. The simulation results show that each vehicle can finish the entire travel trip with a near free flow speed in the proposed method.
In the present scenario, research conducted is mostly based on determining the duration of green light.
Moreover the research papers published on Adaptive Traffic Management did not focus much on the
concept of handling Emergency Vehicles. This major role of this project is as a continuation to the
existing research papers published on this topic. Here we not only handle traffic effectively but also
elaborate on effective management of highly prioritized vehicles through all possible phases. In this
particular research paper, Wireless Sensor Networks (WSN) is assumed to be the source of input.
The document summarizes an optimization program that airlines can use to determine the right freight capacity, operating frequency, and fleet positioning to minimize costs and maximize profits. The program takes in data on routes, yields, demands, and costs. It then runs integer programming models and U-curve techniques to find the optimum solution. A case study on Yemenia airline shows how the program can determine the best aircraft types for its network and maximize profits on a multi-stop route from Sana'a to Singapore.
Enhancing three variants of harmony search algorithm for continuous optimizat...IJECEIAES
Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has a slow convergence rate, which causes the algorithm to have a weak exploitation process in finding the global optima. Different variants of HS introduced in the literature to enhance the algorithm and fix its problems, but in most cases, the algorithm still has a slow convergence rate. Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the performance of different optimization algorithms, including HS. In this work, we adopted a new improved version of OBL, to improve three variants of Harmony Search, by increasing the convergence rate speed of these variants and improving overall performance. The new OBL version named improved opposition-based learning (IOBL), and it is different from the original OBL by adopting randomness to increase the solution's diversity. To evaluate the hybrid algorithms, we run it on benchmark functions to compare the obtained results with its original versions. The obtained results show that the new hybrid algorithms more efficient compared to the original versions of HS. A convergence rate graph is also used to show the overall performance of the new algorithms.
Car Dynamics using Quarter Model and Passive Suspension, Part II: A Novel Sim...IOSR Journals
This document presents research on using a quarter-car model and passive suspension to analyze the dynamics of a vehicle crossing a novel simple harmonic speed hump. The study uses MATLAB simulation of a quarter-car model to investigate the effect of hump dimensions and vehicle speed on ride comfort. It is found that a simple harmonic hump of 9m length allows vehicle speeds up to 30 km/h while maintaining ride comfort. A diagram is presented showing maximum vehicle speed for different hump dimensions that meet ride comfort standards.
This document discusses intelligent traffic light control using multi-agent reinforcement learning. It summarizes three research papers on the topic. The first paper proposes a distributed Q-learning approach that considers both motorized and non-motorized traffic to achieve near-global optimization. The second designs a two-stage negotiation system where traffic lights determine green times based on real-time traffic conditions. The third applies particle swarm optimization to find optimal light cycles for large vehicular networks under various scenarios.
1) The document summarizes a simulation of shuttle bus operations for the University of Cincinnati's North Route shuttle using Arena simulation software.
2) Three scenarios were modeled - a single large 26-seat bus, two large 26-seat buses, and two smaller 14-seat buses.
3) Based on the statistical analysis of 100 replications of each scenario, using two smaller 14-seat buses achieved the best utilization with the lowest average number of empty seats while maintaining a reasonable average passenger waiting time.
Vehicle route scheduling and transportation cost minimization in a latex indu...IJRES Journal
The vehicle route scheduling problem is concerned with the determination of routes and schedules for a fleet of vehicles to satisfy the demands of a set of customers. The goal of vehicle routing is to schedule multiple suppliers from various places. Vehicle routing has existed since the advent of the Industrial age, when large-scale production became possible. As the complexity and scale of the manufacturing world increased, the task of optimizing vehicle routing grew. The vehicle routing problem is a combinatorial optimization and integer programming problem seeking to service a number of customers with a fleet of vehicles. Often the context is that of delivering goods located at a central depot to customers who have placed orders for such goods or vice-versa. Implicit is the goal of minimizing the cost of distributing the goods. Many methods have been developed for searching for good solutions to the problem, however even for the smallest problems, finding global minimum for the cost function is computationally complex. The paper presents an optimization algorithm using Particle Swarm Optimization (PSO) for the vehicle routing that would enable the logistic manager of a latex industry to minimize the transportation cost and maximize the collection using minimum number of vehicles.
Solving real world delivery problem using improved max-min ant system with lo...ijaia
This paper presents a solution to real-world delive
ry problems (RWDPs) for home delivery services wher
e
a large number of roads exist in cities and the tra
ffic on the roads rapidly changes with time. The
methodology for finding the shortest-travel-time to
ur includes a hybrid meta-heuristic that combines a
nt
colony optimization (ACO) with Dijkstra’s algorithm
, a search technique that uses both real-time traff
ic
and predicted traffic, and a way to use a real-worl
d road map and measured traffic in Japan. We
previously proposed a hybrid ACO for RWDPs that use
d a MAX-MIN Ant System (MMAS) and proposed a
method to improve the search rate of MMAS. Since tr
affic on roads changes with time, the search rate i
s
important in RWDPs. In the current work, we combine
the hybrid ACO method with the improved MMAS.
Experimental results using a map of central Tokyo a
nd historical traffic data indicate that the propos
ed
method can find a better solution than conventional
methods.
This document proposes an improved hybrid behavior ant colony algorithm to solve vehicle routing problems. It defines four types of ant behaviors - random, greedy, pheromone-based, and a hybrid behavior considering factors like distance, saving value, and vehicle load. The algorithm allows ants to select behaviors and routes probabilistically based on these factors. Simulation experiments on a 31-city dataset show the hybrid behavior outperforms basic ant colony and other variants, finding better solutions on average. The results demonstrate this improved algorithm can effectively solve vehicle routing problems.
A STUDY AND IMPLEMENTATION OF THE TRANSIT ROUTE NETWORK DESIGN PROBLEM FOR A ...cscpconf
The design of public transportation networks presupposes solving optimization problems,
involving various parameters such as the proper mathematical description of networks, the
algorithmic approach to apply, and also the consideration of real-world, practical
characteristics such as the types of vehicles in the network, the frequencies of routes, demand,
possible limitations of route capacities, travel decisions made by passengers, the environmental
footprint of the system, the available bus technologies, besides others. The current paper
presents the progress of the work that aims to study the design of a municipal public
transportation system that employs middleware technologies and geographic information
services in order to produce practical, realistic results. The system employs novel optimization
approaches such as the particle swarm algorithms and also considers various environmental
parameters such as the use of electric vehicles and the emissions of conventional ones.
A study and implementation of the transit route network design problem for a ...csandit
The design of public transportation networks presup
poses solving optimization problems,
involving various parameters such as the proper mat
hematical description of networks, the
algorithmic approach to apply, and also the conside
ration of real-world, practical
characteristics such as the types of vehicles in th
e network, the frequencies of routes, demand,
possible limitations of route capacities, travel de
cisions made by passengers, the environmental
footprint of the system, the available bus technolo
gies, besides others. The current paper
presents the progress of the work that aims to stud
y the design of a municipal public
transportation system that employs middleware techn
ologies and geographic information
services in order to produce practical, realistic r
esults. The system employs novel optimization
approaches such as the particle swarm algorithms an
d also considers various environmental
parameters such as the use of electric vehicles and
the emissions of conventional ones.
Vehicle routing problem is a NP-hard problem, with the expansion of problem solving more difficult.
This paper proposes a hybrid behavior based on ant colony algorithm to solve the problem, ant to different
objectives in the first place as the path selection according to the analysis of the impact on the algorithm, then
define the ant behavior and design four concrete ant behavior by selecting different ways of ant behavior to
form different improved algorithm. Finally, experimental results show that the improved algorithm can solve
vehicle routing problems quickly and effectively.
An improved ant colony algorithm based onIJCI JOURNAL
This paper presents an improved chaotic ant colony system algorithm (ICACS) for solving combinatorial
optimization problems. The existing algorithms still have some imperfections, we use a combination of two
different operators to improve the performance of algorithm in this work. First, 3-opt local search is used
as a framework for the implementation of the ACS to improve the solution quality; Furthermore, chaos is
proposed in the work to modify the method of pheromone update to avoid the algorithm from dropping into
local optimum, thereby finding the favorable solutions. From the experimental results, we can conclude
that ICACS has much higher quality solutions than the original ACS, and can jump over the region of the
local optimum, and escape from the trap of a local optimum successfully and achieve the best solutions.
Therefore, it’s better and more effective algorithm for TSP.
Route optimization problem using vehicle routing problem (VRP) and time window constraint is explained as finding paths for a finite count of vehicles to provide service to a huge number of customers and hence, optimizing the
path in a given duration of the time window. The vehicles in the loop have restricted intake of capacity. This path initiates from the depot, delivers the
goods, and stops at the depot. Each customer is to serve exactly once. If the arrival of the vehicle is before the time window “opens” or when the time window “closes,” there will be waiting for cost and late cost. The challenge involved over here is to scheduling visits to customers who are only
available during specific time windows. Ant colony optimization (ACO) algorithm is a meta-heuristic algorithm stimulated by the growing behaviour of real ants. In this paper, we combine the ACO algorithm with graph network henceforth increasing the number of vehicles in a particular depot for increasing the efficiency for timely delivery of the goods in a particular
time width. This problem is solved by, an efficient technique known as the ACO+graph algorithm.
A Minimum Spanning Tree Approach of Solving a Transportation Probleminventionjournals
: This work centered on the transportation problem in the shipment of cable troughs for an underground cable installation from three supply ends to four locations at a construction site where they are needed; in which case, we sought to minimize the cost of shipment. The problem was modeled into a bipartite network representation and solved using the Kruskal method of minimum spanning tree; after which the solution was confirmed with TORA Optimization software version 2.00. The result showed that the cost obtained in shipping the cable troughs under the application of the method, which was AED 2,022,000 (in the United Arab Emirate Dollar), was more effective than that obtained from mere heuristics when compared.
Help the Genetic Algorithm to Minimize the Urban Traffic on IntersectionsIJORCS
This document summarizes a research paper that uses genetic algorithms to optimize traffic light timing at intersections to minimize traffic. It first describes modeling traffic light intersections using Petri nets. It then explains how genetic algorithms can be used for optimization by coding the problem variables in chromosomes, defining a fitness function to evaluate populations over generations, and using operators like mutation and crossover. The fitness function aims to minimize average traffic light cycle times based on 14 parameters related to light timing and vehicle wait times at two intersections. The genetic algorithm optimization of traffic light timing parameters is found to improve traffic flow at intersections.
The document describes an intelligent real-time traffic management system for complex railway networks developed through a collaboration between Alstom and Roma Tre University. It presents an alternative graph model and mixed integer linear programming formulations to represent railway operations and detect/resolve conflicts. Computational results show the AGLIBRARY solver can find optimal solutions to train scheduling and routing problems involving up to 60 trains in large networks within 30 seconds, while a commercial solver required over 15 minutes on average.
Chaotic ANT System Optimization for Path Planning of the Mobile Robotscseij
This paper presents an improved ant system algorithm for path planning of the mobile robot under the complicated environment. To solve the drawback of the traditional ant colony system algorithm (ACS), which usually falls into the local optimum, we propose an improved ant colony system algorithm (IACS) based on chaos. Simulation experiments show that chaotic ant colony algorithm not only enhances the global search capability, but also has more effective than the traditional algorithm.
Algorithms And Optimization Techniques For Solving TSPCarrie Romero
The document discusses three algorithms - simulated annealing, ant colony optimization, and genetic algorithm - for solving the traveling salesman problem (TSP). It analyzes each algorithm's approach, parameters used, and results of experiments on 15 and 50 randomly generated cities. Simulated annealing had average distances of 4.1341 and 20.1316 units for 15 and 50 cities respectively. Ant colony optimization yielded average distances of 3.9102 units for 15 cities, running faster than simulated annealing. Genetic algorithm was tested on 15 cities in Brazil.
A MODIFIED ANT COLONY ALGORITHM FOR SOLVING THE UNIT COMMITMENT PROBLEMAEIJjournal2
Solving the unit commitment (UC) problem is one of the most complicated issues in power systems that its
exact solving can be calculated by perfect counting of entire possible compounds of generative units. UC is
equated as a nonlinear optimization with huge size. Purpose of solving this problem is to programming the
optimization of the generative units to minimize the full action cost regarding problem constraints. In this
article, a modified version of ant colony optimization (MACO) is introduced for solving the UC problem in
a power system. ACO algorithm is a powerful optimization method which has the capability of fleeing from
local minimums by performing flexible memory system. The efficiency of proposed method in two power
system containing 4 and 10 generative units is indicated. Comparison of obtained results from the proposed
method with results of the past well-known methods is a proof for suitability of performing the introduced
algorithm in economic input and output of generative units.
Advanced Energy: An International Journal (AEIJ)AEIJjournal2
A modified ant colony optimization algorithm is proposed to solve the unit commitment problem in power systems. The unit commitment problem aims to minimize generation costs while meeting demand and operational constraints of generation units. The modified ant colony optimization algorithm uses multiple pheromone matrices, one for each hour, to represent the problem. It also uses discrete power levels for units and considers their minimum up and down times. The algorithm evaluates solutions based on generation costs while ensuring demand is met and system reserves are maintained at each step. The algorithm is tested on 4 and 10 unit systems and shows improvements over previous ant colony optimization methods for unit commitment.
A MODIFIED ANT COLONY ALGORITHM FOR SOLVING THE UNIT COMMITMENT PROBLEMAEIJjournal2
Solving the unit commitment (UC) problem is one of the most complicated issues in power systems that its exact solving can be calculated by perfect counting of entire possible compounds of generative units. UC is equated as a nonlinear optimization with huge size. Purpose of solving this problem is to programming the optimization of the generative units to minimize the full action cost regarding problem constraints. In this article, a modified version of ant colony optimization (MACO) is introduced for solving the UC problem in a power system. ACO algorithm is a powerful optimization method which has the capability of fleeing from
local minimums by performing flexible memory system. The efficiency of proposed method in two power system containing 4 and 10 generative units is indicated. Comparison of obtained results from the proposed
method with results of the past well-known methods is a proof for suitability of performing the introduced
algorithm in economic input and output of generative units.
A MODIFIED ANT COLONY ALGORITHM FOR SOLVING THE UNIT COMMITMENT PROBLEMaeijjournal
Solving the unit commitment (UC) problem is one of the most complicated issues in power systems that its
exact solving can be calculated by perfect counting of entire possible compounds of generative units. UC is
equated as a nonlinear optimization with huge size. Purpose of solving this problem is to programming the
optimization of the generative units to minimize the full action cost regarding problem constraints. In this
article, a modified version of ant colony optimization (MACO) is introduced for solving the UC problem in
a power system. ACO algorithm is a powerful optimization method which has the capability of fleeing from
local minimums by performing flexible memory system. The efficiency of proposed method in two power
system containing 4 and 10 generative units is indicated. Comparison of obtained results from the proposed
method with results of the past well-known methods is a proof for suitability of performing the introduced
algorithm in economic input and output of generative units.
Swarm Intelligence Technique ACO and Traveling Salesman ProblemIRJET Journal
The document discusses the ant colony optimization (ACO) algorithm, a swarm intelligence technique inspired by ant behavior, and its application to the traveling salesman problem (TSP). ACO mimics how ants deposit and follow pheromone trails to probabilistically determine paths, and it has been shown to find good solutions to TSP. The paper also reviews the ACO algorithm and describes how it can be applied to find the shortest tour between cities in TSP.
The document summarizes research on using ant colony optimization (ACO) supervised by particle swarm intelligence (PSI) to solve multi-objective vehicle routing problems. It proposes applying this approach to determine optimal routes on a linearly expanded network model. The ACO algorithm finds shortest paths between nodes while avoiding local optima, guided by PSI. Experimental results show the ACOLS-PSI algorithm improves average route distance by 8% compared to existing greedy algorithms. Future work could combine this approach with other shortest path methods into a memetic algorithm to better solve wide and sparse vehicle routing networks.
Urban Bus Route Planning Using Reverse Labeling Dijkstra Algorithm for Tempor...IRJET Journal
The document discusses using the Reverse Labeling Dijkstra Algorithm (RLDA) to optimize urban bus route planning by considering real-time traffic conditions. RLDA is an adaptation of Dijkstra's algorithm that finds the shortest path from the destination node backwards towards the source node. This allows it to consider arc attributes representing traffic congestion levels. The document presents the mathematical model and steps of the RLDA algorithm. It then discusses simulating the RLDA approach on a real-time road network to determine efficient bus routes.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
2. 158 Computer Science & Information Technology (CS & IT)
In order to solve the combinatorial optimization problem, we first cast it in the form of a
Travelling Salesman Problem and use some of the soft–computing techniques to find the optimal
solution. Although the TSP has applications in practical problems like Vehicle Routing, Job
Sequencing, Computer Wiring, etc. [3], it is known to be NP hard. Since brute force approach is
an infeasible option, heuristics approach can be fairly relied upon to solve thesetypes of problems
since heuristics approach utilizes much less computing power. Some of the conventional heuristic
techniques designed to solve the TSP include branch and cut [4], dynamic programming [5],
regression analysis [6], exact methods [7], etc. Recently many meta-heuristic algorithms (i.e.
heuristics that do not depend on the domain knowledge of the problem) are successfully
employed to search for the optimal TSP solution. The Genetic Algorithm (GA) based on the
Darwinian theory of natural selection and its variants are reported to be successful in finding the
optimal solutions to the benchmark TSP problems in a reasonable amount of computing time [8-
11]. In some studies, the Genetic Algorithm is combined with other meta-heuristic optimization
algorithms to improve the optimization results [12].
However, the most successful soft computing algorithm to obtain the optimal solution of the TSP
is the Ant Colony Optimization (ACO) algorithm. The development of the ACO algorithm has
been inspired by the foraging behaviour of some ant species. These ants deposit pheromone on
the ground in order to mark some favourable path that should be followed by other members of
the colony. The ACO algorithm exploits a similar mechanism for solving optimization problems
[13-19]. From the early nineties, when the first ACO algorithm was proposed, it has attracted the
attention of an increasing numbers of researchers and it has been extended to many successful
applications.
In this study, the railway capacity optimization problem is cast in the form of a TSP. The
arrival/departure events in the schedule are treated as nodes which need to be ordered under the
given scheduling constraints so as to minimize the entire schedule time. Some of the other
constraints are imposed by the track-changing hardware equipment. The time between two events
is considered to be the distance between two TSP edges and the train operation schedule is
considered to be the tour length of the TSP. The standard ACO application to this problem yields
an optimal schedule, under the given infrastructure and operational constraints.
This paper is organized as follows: Section 2 describes the TSP and ACO. Section 3 describes the
formulation of the railway capacity optimization problem (RCP) as the TSP and its solution using
the standard ACO algorithm. Section 4 presents the simulation optimization results and section 5
concludes the paper.
2. TSP AND ACO
In this section, we introduce the Travelling Salesman Problem and the Ant Colony Optimization
algorithm. We show how the Ant Colony Optimization algorithm is designed to solve the
Travelling Salesman Problem.
2.1. Travelling Salesman Problem (TSP)
The Travelling Salesman Problem (TSP) is a classic problem in computer science which may be
stated as follows: Given a list of cities and their pairwise distances, the task is to find the
3. Computer Science & Information Technology (CS & IT) 159
Figure 1. Three feasible routes of a 6-node TSP
shortest possible route that visits each city exactly once and then return to the original city. If n is
the number of cities to be visited, the total number of possible routes covering all cities, Snis
given by:
Sn= (n-1)!/2 (1)
A naive solution solves the problem in O(n!) time, simply by checking all possible routes, and
selecting the shortest one. A more efficient dynamic programming approach yields a solution in
O(n22n) time [3]. The TSP is proved to be NP-hard and various Operation Research (OR) solution
techniques have been proposed, yielding varying degrees of success [4-7]. The Ant Colony
Optimization, described in the following sub-section is a novel soft computing algorithm
developed to tackle combinatorial optimization problems.
2.2. Ant Colony Optimization CO
The Ant Colony Optimization (ACO) which is based on the foraging behaviour of ants was first
proposed by Dorigo [13].
1 Initialize parameters and solutions
2 While the termination criterion is not met
3 Evaluate solutions
4 Update pheromone
5 Construct new solutions
6 End
7 Output the optimum solution
Figure 2. The ACO algorithm
A generic ACO algorithm is shown in Figure. 2. In step 1, the algorithm parameters are initialized
and all the artificial ants (random solutions) are generated. The loop from lines 2 through 6 is
repeated until the termination condition is met. The steps inside the loop consist of evaluating the
solutions, updating the pheromones and constructing new solutions from the previous solutions.
The two main steps inside the loop are further described below.
Solution construction
Ant k on node i selects node j, based on the probability, pij, given by:
4. 160 Computer Science & Information Technology (CS & IT)
where denotes the set of candidate sub-solutions; τij and ηij denote, respectively, the pheromone
value and the heuristic value associated with eij.
Updating the pheromone
The pheromone update operator employed for updating the pheromone value of each edge eijis
defined as
Where Lkdenotes the quality of the solution created by ant k; denotes the evaporation
rate.
3. CAPACITY PROBLEM AS TSP
This section describes in detail the railway capacity problem to be optimized. It explains the
optimization constraints, the framing of the railway capacity problem as a typical TSP and finally
the solution process by using the standard ACO algorithm.
3.1. Capacity Problem
When dealing with the railway capacity problem, the railway management has to consider the
different types of capacities in the railway domain. Some of the relevant capacities, for instance,
are: (1) the capacity of the platform to hold passengers, (2) the capacity of the carriages to hold
passengers, (3) the rail network capacity to hold the number of trains at a given time, and (4) the
capacity of the railway station structure to schedule the maximum number of trains per unit time.
Dealing with all these types of capacities simultaneously is a complex problem. This study is
dedicated to the maximization of only the type 4 railway capacity, i.e., maximization of the
number of trains that can be scheduled at a railway station per unit time. The type 4 rail capacity
optimization in turn leads to optimization of the royalties and alleviation of the congestion
problem during rush hours.
5. Computer Science & Information Technology (CS & IT) 161
3.2. Capacity problem as TSP
In the generalized form of the TSP, the cities are represented as nodes (Figure 3a). The task is
then finding the shortest route, starting from a given node and visiting each node in the network
exactly once before returning to the starting node. In the Railway Capacity Optimization (RCP)
problem, the arrival/departure events (Figure 3b) in the schedule are treated as nodes which need
to be ordered under the given scheduling constraints so as to minimize the entire schedule time.
Figure 3a.The TSP nodes (cities) Figure 3b. The RCP nodes (events)
3.3. Structure constraints
In this study, we consider a railway terminal station with four railroads, each with an attached
platform. The trains can arrive at the terminal and leave the terminal via any of these four
railroads. There are five train services, namely, S55, S5, L7, S2 and S1 and the railroad access
constraints are given in Table 1.
Figure 4. The platforms in the terminal station
.
6. 162 Computer Science & Information Technology (CS & IT)
Table 1. Given parameters of the train capacity problem
4. OPTIMIZATION RESULTS
The aim of the simulation experiments using the ACO algorithm is to maximize the number of
trains leaving the terminal station in an hour. However, to reduce the calculation load, we divide
the hourly interval into 5 equal intervals, each being of 720 seconds (12 minutes) duration. The
assumption here is that the train schedule is periodic. The same period can then be stretched over
an hour. In Table 3, the final capacity of the terminal station is calculated by using the following
formula:
where, T is the total time for the entire schedule covering a period of 720 seconds.
The minimum time for the entire schedule over a period of 720 seconds is found to be 555
seconds and correspondingly the maximum capacity is 38.9 trains/hour
We conducted several experiments by varying the and parameters of the ACO algorithm.
Some of the optimal results obtained by these tuned parameters are shown in Table 4. Another
important parameter that needs an empirical tuning is the population size of the agents, N. Table 5
shows the results obtained by varying this number. As expected, the larger the population size,
the better the results are, although this increases the computational overhead.
Table 2. Varying the population size of the ACO agents
7. Computer Science & Information Technology (CS & IT) 163
5. CONCLUSIONS
The Ant Colony Optimization soft computing algorithm is apt for solving combinatorial
optimization problems like the classical NP-hard Travelling Salesman Problem. Basing the search
on the stigmery of the food foraging real-life ant colony, the algorithm explores the huge search
space of the NP-hard problems to find the optimal solution. In this study, the authors have applied
the ACO algorithm to optimize the capacity of a terminal railway station. The capacity
optimization problem is cast into the form of a TSP-like problem, where the arrival and departure
events of the trains are considered to be the nodes and the schedule length as the TSP total route.
The standard ACO optimizes the schedule length subject to the infrastructure and operational
constraints. The simulation experiments validate the formulation of the railway capacity problem
as a TSP. The optimal solutions obtained by the soft-computing technique is superior to those
produced by the domain experts.
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AUTHORS
Dr. Tad Gonsalvesobtained the BS degree in theoretical Physics and the MS degree in Astrophysics from
the Pune University. He obtained the PhD in Systems Engineering from Sophia University, Tokyo, Japan.
Currently he is an Assistant Professor in the Department of Information and Communication Sciences,
Faculty of Science & Technology in the same university. His research interests include design of Expert
Systems, Evolutionary Algorithms, Machine Learning and Parallel Programming.
TakafumiShiozakiis an under-graduate student is the Department of Information and Communication
Sciences, Faculty of Science & Technology, Sophia University, Tokyo, Japan. His research is on the
application of the Evolutionary Algorithms to diverse real-world problems.