scholarly journals Cooperative Passenger Inflow Control in Urban Mass Transit Network with Constraint on Capacity of Station

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Jianyuan Guo ◽  
Limin Jia ◽  
Yong Qin ◽  
Huijuan Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Waiting Time ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Mass Transit ◽  
Constrained Optimization Problem ◽  
Swarm Optimization ◽  
Transit Network ◽  
Urban Mass Transit ◽  
Transfer Stations

In urban mass transit network, when passengers’ trip demands exceed capacity of transport, the numbers of passengers accumulating in the original or transfer stations always exceed the safety limitation of those stations. It is necessary to control passenger inflow of stations to assure the safety of stations and the efficiency of passengers. We define time of delay (TD) to evaluate inflow control solutions, which is the sum of waiting time outside of stations caused by inflow control and extra waiting time on platform waiting for next coming train because of insufficient capacity of first coming train. We build a model about cooperative passenger inflow control in the whole network (CPICN) with constraint on capacity of station. The objective of CPICN is to minimize the average time of delay (ATD) and maximum time of delay (MTD). Particle swarm optimization for constrained optimization problem is used to find the optimal solution. The numeral experiments are carried out to prove the feasibility and efficiency of the model proposed in this paper.

scholarly journals Hybrid method for achieving Pareto front on economic emission dispatch

2020 ◽  
Vol 10 (4) ◽  
pp. 3358
Author(s):  
Kummari Rajesh ◽  
N. Visali
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Hybrid Method ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Test System ◽  
Swarm Optimization ◽  
Metaheuristic Methods

In this paper hybrid method, Modified Nondominated Sorted Genetic Algorithm (MNSGA-II) and Modified Population Variant Differential Evolution(MPVDE) have been placed in effect in achieving the best optimal solution of Multiobjective economic emission load dispatch optimization problem. In this technique latter, one is used to enforce the assigned percent of the population and the remaining with the former one. To overcome the premature convergence in an optimization problem diversity preserving operator is employed, from the tradeoff curve the best optimal solution is predicted using fuzzy set theory. This methodology validated on IEEE 30 bus test system with six generators, IEEE 118 bus test system with fourteen generators and with a forty generators test system. The solutions are dissimilitude with the existing metaheuristic methods like Strength Pareto Evolutionary Algorithm-II, Multiobjective differential evolution, Multi-objective Particle Swarm optimization, Fuzzy clustering particle swarm optimization, Nondominated sorting genetic algorithm-II.

Research on Optimization of Urban Mass Transit Network Schedule Based on Coordination of Connecting Time Between Different Lines

2010 ◽  
Author(s):  
Xiaohong Fang ◽  
Leishan Zhou ◽  
Ming Xia
Keyword(s):  
Waiting Times ◽  
Mass Transit ◽  
Service Level ◽  
Small Time ◽  
Optimization Techniques ◽  
Transit System ◽  
Transit Network ◽  
Schedule Coordination ◽  
Urban Mass Transit ◽  
Transfer Stations

On urban mass transit network, some connections inherently exist between trains on different lines through transfer stations. Schedule coordination for different lines, especially optimizing the arriving and departure times at transfer stations, may significantly reduce transfer waiting times at stations where various routes are interconnected, so as to improve the passenger service level. Based on the passengers flow characters of urban mass transit (UMT), both the convenience and rationality of connection between different lines were considered, and then an optimization model, with the aims of the minimal total waiting time of transfer passengers and inboard passengers, was set up. Combining the inner coordination of arriving and departure time sequence of trains in transfer nodes with the exterior coordination of transfer nodes on whole urban mass transit network, a multi-layers coordination policy was proposed, and the integrated optimization of the urban mass transit system was realized through taking some small time shifts of the proposed singleline timetables. In order to verify the validity and feasibility of the model and algorithm, we conducted an experimental study. The result turns out that improvement on UMT network can be determined by such optimization techniques.

scholarly journals A general framework of particle swarm optimization

2021 ◽  
Author(s):  
Loc Nguyen
Keyword(s):  
Particle Swarm Optimization ◽  
General Framework ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Target Function ◽  
Optimal Solution ◽  
Exploration And Exploitation ◽  
Global Optimal Solution ◽  
Swarm Optimization ◽  
Global Optimal

Particle swarm optimization (PSO) is an effective algorithm to solve the optimization problem in case that derivative of target function is inexistent or difficult to be determined. Because PSO has many parameters and variants, I propose a general framework of PSO called GPSO which aggregates important parameters and generalizes important variants so that researchers can customize PSO easily. Moreover, two main properties of PSO are exploration and exploitation. The exploration property aims to avoid premature converging so as to reach global optimal solution whereas the exploitation property aims to motivate PSO to converge as fast as possible. These two aspects are equally important. Therefore, GPSO also aims to balance the exploration and the exploitation. It is expected that GPSO supports users to tune parameters for not only solving premature problem but also fast convergence.

The Research of First and Last Trip Optimization under the Network of Urban Mass Transit

2013 ◽  
Vol 409-410 ◽  
pp. 1311-1314 ◽  
Author(s):  
Yong Feng Shang ◽  
Lei Shan Zhou ◽  
Lu Tong ◽  
Chang Jun Cai
Keyword(s):  
Optimal Solution ◽  
Mass Transit ◽  
Time Range ◽  
Feasible Region ◽  
Rail Network ◽  
Urban Mass Transit ◽  
Time Quantum ◽  
Network Operation ◽  
Local Optimal Solution ◽  
Transfer Stations

Under the background of the urban mass transit becoming network increasingly, time coordination of the first and last trains becomes the important part of urban mass transit network operation, and has special significance. This paper firstly ascertains the time range of first and last running of trains and analyzes from two aspects. Then find the time quantum that the passengers and companies all accept. The paper analyzes the departed principle of networked first and last running time of trains, the target is the minimum of the passengers total loss of time at the transfer stations, setting up a multiple-direction trains transfer mathematical model that achieves attainability and rationality for transferring among different lines and getting a local optimal solution by using the method of iteration, and the optimal solution is within the feasible region and fits actual situations. Lastly a specific urban rail network is taken as an example to check the model and algorithm.

scholarly journals Multiconstrained Ascent Trajectory Optimization Using an Improved Particle Swarm Optimization Method

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mu Lin ◽  
Zhao-Huanyu Zhang ◽  
Hongyu Zhou ◽  
Yongtao Shui
Keyword(s):  
Particle Swarm Optimization ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Flight Path Angle ◽  
Gaussian Perturbation

This paper researches the ascent trajectory optimization problem in view of multiple constraints that effect on the launch vehicle. First, a series of common constraints that effect on the ascent trajectory are formulated for the trajectory optimization problem. Then, in order to reduce the computational burden on the optimal solution, the restrictions on the angular momentum and the eccentricity of the target orbit are converted into constraints on the terminal altitude, velocity, and flight path angle. In this way, the requirement on accurate orbit insertion can be easily realized by solving a three-parameter optimization problem. Next, an improved particle swarm optimization algorithm is developed based on the Gaussian perturbation method to generate the optimal trajectory. Finally, the algorithm is verified by numerical simulation.

Study on Connection Optimization of Last Train Departure Time on Urban Mass Transit Network

2014 ◽  
Vol 1030-1032 ◽  
pp. 2211-2214 ◽  
Author(s):  
Chun Ge Kou ◽  
Shi Wei He ◽  
Bi Sheng He
Keyword(s):  
Numerical Analysis ◽  
Waiting Time ◽  
Mass Transit ◽  
Volume Distribution ◽  
Distribution Method ◽  
Transit Network ◽  
Departure Time ◽  
Urban Mass Transit ◽  
Passenger Volume ◽  
Operation And Management

The operation and management of urban mass transit network put forward higher requirements for last trains‘ transfer connection. Based on the analysis of coordination relationship and timeliness of accessible routes, this paper puts forward a dynamic passenger volume distribution method according to the generalized travel cost. Then the connection optimization model of last train departure time is built to increase accessible passenger volume and reduce passengers’ transfer waiting time of all OD pairs for last trains. Finally, the validity and rationality of this model and algorithm is verified with numerical analysis.

scholarly journals DIMENSIONED PARTICLE SWARM OPTIMIZATION FOR REACTIVE POWER OPTIMIZATION PROBLEM

2018 ◽  
Vol 6 (4) ◽  
pp. 281-290
Author(s):  
K. Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Search Space ◽  
Reactive Power Optimization ◽  
Swarm Optimization ◽  
Local Optimal Solution

This paper present’s Dimensioned Particle Swarm Optimization (DPSO) algorithm for solving Reactive power optimization (RPO) problem.  Dimensioned extension is introduced to particles in the particle swarm optimization (PSO) model in order to overcome premature convergence in interactive optimization. In the performance of basic PSO often flattens out with a loss of diversity in the search space as resulting in local optimal solution.  Proposed algorithm has been tested in standard IEEE 57 test bus system and compared to other standard algorithms. Simulation results reveal about the best performance of the proposed algorithm in reducing the real power loss and voltage profiles are within the limits.

scholarly journals Particle Swarm Optimization Combined with Inertia-Free Velocity and Direction Search

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 597
Author(s):  
Kun Miao ◽  
Qian Feng ◽  
Wei Kuang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Small Region ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Local Searches ◽  
Engineering Design Problems ◽  
Direction Search

The particle swarm optimization algorithm (PSO) is a widely used swarm-based natural inspired optimization algorithm. However, it suffers search stagnation from being trapped into a sub-optimal solution in an optimization problem. This paper proposes a novel hybrid algorithm (SDPSO) to improve its performance on local searches. The algorithm merges two strategies, the static exploitation (SE, a velocity updating strategy considering inertia-free velocity), and the direction search (DS) of Rosenbrock method, into the original PSO. With this hybrid, on the one hand, extensive exploration is still maintained by PSO; on the other hand, the SE is responsible for locating a small region, and then the DS further intensifies the search. The SDPSO algorithm was implemented and tested on unconstrained benchmark problems (CEC2014) and some constrained engineering design problems. The performance of SDPSO is compared with that of other optimization algorithms, and the results show that SDPSO has a competitive performance.

scholarly journals Service Composition in IoT using Genetic algorithm and Particle swarm optimization

2020 ◽  
Vol 10 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Neeti Kashyap ◽  
A. Charan Kumari ◽  
Rita Chhikara
Keyword(s):  
Genetic Algorithm ◽  
Quality Of Service ◽  
Particle Swarm Optimization ◽  
Service Composition ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Swarm Optimization

AbstractWeb service compositions are commendable in structuring innovative applications for different Internet-based business solutions. The existing services can be reused by the other applications via the web. Due to the availability of services that can serve similar functionality, suitable Service Composition (SC) is required. There is a set of candidates for each service in SC from which a suitable candidate service is picked based on certain criteria. Quality of service (QoS) is one of the criteria to select the appropriate service. A standout amongst the most important functionality presented by services in the Internet of Things (IoT) based system is the dynamic composability. In this paper, two of the metaheuristic algorithms namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are utilized to tackle QoS based service composition issues. QoS has turned into a critical issue in the management of web services because of the immense number of services that furnish similar functionality yet with various characteristics. Quality of service in service composition comprises of different non-functional factors, for example, service cost, execution time, availability, throughput, and reliability. Choosing appropriate SC for IoT based applications in order to optimize the QoS parameters with the fulfillment of user’s necessities has turned into a critical issue that is addressed in this paper. To obtain results via simulation, the PSO algorithm is used to solve the SC problem in IoT. This is further assessed and contrasted with GA. Experimental results demonstrate that GA can enhance the proficiency of solutions for SC problem in IoT. It can also help in identifying the optimal solution and also shows preferable outcomes over PSO.

OPTIMAL PREDICTION AND DESIGN OF SURFACE ROUGHNESS FOR CNC TURNING OF AL7075-T6 BY USING THE TAGUCHI HYBRID QPSO ALGORITHM

2016 ◽  
Vol 40 (5) ◽  
pp. 883-895 ◽  
Author(s):  
Wen-Jong Chen ◽  
Chuan-Kuei Huang ◽  
Qi-Zheng Yang ◽  
Yin-Liang Yang
Keyword(s):  
Surface Roughness ◽  
Particle Swarm Optimization ◽  
Predictive Models ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Cutting Parameters ◽  
Optimization Methods ◽  
Swarm Optimization ◽  
Cnc Turning ◽  
Multi Objective

This paper combines the Taguchi-based response surface methodology (RSM) with a multi-objective hybrid quantum-behaved particle swarm optimization (MOHQPSO) to predict the optimal surface roughness of Al7075-T6 workpiece through a CNC turning machining. First, the Taguchi orthogonal array L27 (36) was applied to determine the crucial cutting parameters: feed rate, tool relief angle, and cutting depth. Subsequently, the RSM was used to construct the predictive models of surface roughness (Ra, Rmax, and Rz). Finally, the MOHQPSO with mutation was used to determine the optimal roughness and cutting conditions. The results show that, compared with the non-optimization, Taguchi and classical multi-objective particle swarm optimization methods (MOPSO), the roughness Ra using MOHQPSO along the Pareto optimal solution are improved by 68.24, 59.31 and 33.80%, respectively. This reveals that the predictive models established can improve the machining quality in CNC turning of Al7075-T6.

Sign in / Sign up

Export Citation Format

Share Document