Study on Vehicle Logistics Routing Path Problem with Time Windows Based on Particle Swarm Optimization

2014 ◽  
Vol 687-691 ◽  
pp. 5161-5164
Author(s):  
Lian Zhou Gao
Keyword(s):  
Particle Swarm Optimization ◽  
World Economy ◽  
Time Window ◽  
Time Windows ◽  
Particle Swarm ◽  
Modern Logistics ◽  
Swarm Optimization ◽  
Logistics Industry ◽  
Search Speed ◽  
Simulation Results

As the development of world economy, how to realize the reasonable vehicle logistics routing path problem with time window constrain is the key issue in promoting the prosperity and development of modern logistics industry. Through the research of vehicle logistics routing path 's demand, particle swarm optimization with a novel particle presentation is designed to solve the problem which is improved, effective and adept to the normal vehicle logistics routing. The simulation results of example indicate that the algorithm has more search speed and stronger optimization ability.

scholarly journals Minimization of Logistics Cost and Carbon Emissions Based on Quantum Particle Swarm Optimization

2018 ◽  
Vol 10 (10) ◽  
pp. 3791 ◽  
Author(s):  
Daqing Wu ◽  
Jiazhen Huo ◽  
Gefu Zhang ◽  
Weihua Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Carbon Emissions ◽  
Time Window ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Transport Cost ◽  
Distribution Centers ◽  
Swarm Optimization ◽  
Quantum Particle Swarm Optimization

This paper aims to simultaneously minimize logistics costs and carbon emissions. For this purpose, a mathematical model for a three-echelon supply chain network is created considering the relevant constraints such as capacity, production cost, transport cost, carbon emissions, and time window, which will be solved by the proposed quantum-particle swarm optimization algorithm. The three-echelon supply chain, consisting of suppliers, distribution centers, and retailers, is established based on the number and location of suppliers, the transport method from suppliers to distribution centers, and the quantity of products to be transported from suppliers to distribution centers and from these centers to retailers. Then, a quantum-particle swarm optimization is described as its performance is validated with different benchmark functions. The scenario analysis validates the model and evaluates its performance to balance the economic benefit and environmental effect.

scholarly journals Performance of Resource Allocation in Device-to-Device Communication Systems Based on Evolutionally Optimization Algorithms

2018 ◽  
Author(s):  
Hsu-Tan Tan ◽  
Bor-An Chen ◽  
Yung-Fa Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Communication Systems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
System Capacity ◽  
Swarm Optimization ◽  
Device To Device ◽  
Simulation Results ◽  
Capacity Performance

In this study, the resource blocks (RB) are allocated to user equipment (UE) according to the evolutional algorithms for long term evolution (LTE) systems. Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms, based on the imitation of a flock of birds foraging behavior through learning and grouping the best experience. In previous work, the Simple Particle Swarm Optimization (SPSO) algorithm was proposed for RB allocation to enhance the throughput of Device-to-Device (D2D) communications and improve the system capacity performance. In simulation results, with less population size of M = 10, the SPSO can perform quickly convergence to sub-optimal solution in the 100th generation and obtained sub-optimum performance with more 2 UEs than the Rand method. Genetic algorithm (GA) is one of the evolutionary algorithms, based on Darwinian models of natural selection and evolution. Therefore, we further proposed a Refined PSO (RPSO) and a novel GA to enhance the throughput of UEs and to improve the system capacity performance. Simulation results show that the proposed GA with 100 populations, in 200 generations can converge to suboptimal solutions. Therefore, with comparing with the SPSO algorithm the proposed GA and RPSO can improve system capacity performance with 1.8 and 0.4 UEs, respectively.

Convergence Analysis of Particle Swarm Optimization via Illustration Styles

2021 ◽  
pp. 29-37
Author(s):  
Wei-Der Chang ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Analysis ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Evolutionary Computations ◽  
Swarm Optimization ◽  
Simulation Results ◽  
Population Based Algorithm

Particle swarm optimization (PSO) is the most important and popular algorithm to solving the engineering optimization problem due to its simple updating formulas and excellent searching capacity. This algorithm is one of evolutionary computations and is also a population-based algorithm. Traditionally, to demonstrate the convergence analysis of the PSO algorithm or its related variations, simulation results in a numerical presentation are often given. This way may be unclear or unsuitable for some particular cases. Hence, this paper will adopt the illustration styles instead of numeric simulation results to more clearly clarify the convergence behavior of the algorithm. In addition, it is well known that three parameters used in the algorithm, i.e., the inertia weight w, position constants c1 and c2, sufficiently dominate the whole searching performance. The influence of these parameter settings on the algorithm convergence will be considered and examined via a simple two-dimensional function optimization problem. All simulation results are displayed using a series of illustrations with respect to various iteration numbers. Finally, some simple rules on how to suitably assign these parameters are also suggested

Constraint Handling in Particle Swarm Optimization

2012 ◽  
pp. 60-80
Author(s):  
Wen Fung Leong ◽  
Gary G. Yen
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Constraint Handling ◽  
Hidden Information ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Local Searches ◽  
Multiobjective Approach ◽  
Simulation Results ◽  
Fine Tune

In this article, the authors propose a particle swarm optimization (PSO) for constrained optimization. The proposed PSO adopts a multiobjective approach to constraint handling. Procedures to update the feasible and infeasible personal best are designed to encourage finding feasible regions and convergence toward the Pareto front. In addition, the infeasible nondominated solutions are stored in the global best archive to exploit the hidden information for guiding the particles toward feasible regions. Furthermore, the number of feasible personal best in the personal best memory and the scalar constraint violations of personal best and global best are used to adapt the acceleration constants in the PSO flight equations. The purpose is to find more feasible particles and search for better solutions during the process. The mutation procedure is applied to encourage global and fine-tune local searches. The simulation results indicate that the proposed constrained PSO is highly competitive, achieving promising performance.

Optimum Setting of DC Drives PID Controller via Particle Swarm Optimization

2012 ◽  
Vol 157-158 ◽  
pp. 88-93 ◽  
Author(s):  
Guang Hui Chang ◽  
Jie Chang Wu ◽  
Chao Jie Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Pid Controller ◽  
Position Control ◽  
Particle Swarm ◽  
Dc Motor ◽  
Motor Drive ◽  
Proportional Integral Derivative ◽  
Stable Convergence ◽  
Swarm Optimization ◽  
Simulation Results

In this paper, an intelligent controller of PM DC Motor drive is designed using particle swarm optimization (PSO) method for tuning the optimal proportional-integral-derivative (PID) controller parameters. The proposed approach has superior feature, including easy implementation, stable convergence characteristics and very good computational performances efficiency.To show the validity of the PID-PSO controller, a DC motor position control case is considered and some simulation results are shown. The DC Motor Scheduling PID-PSO controller is modeled in MATLAB environment.. It can be easily seen from the simulation results that the proposed method will have better performance than those presented in other studies.

Particle Swarm Optimization Based Algorithm for Ship Vertical Passage Layout Optimization

2012 ◽  
Vol 479-481 ◽  
pp. 1885-1888
Author(s):  
Wen Quan Wang ◽  
Sheng Huang ◽  
Yu Long Hu ◽  
Yuan Hang Hou
Keyword(s):  
Particle Swarm Optimization ◽  
Rational Design ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Vital Role ◽  
Layout Problem ◽  
Swarm Optimization ◽  
Operation And Maintenance ◽  
Simulation Results ◽  
The Mathematical Model

The passage plays a vital role in operation and maintenance of naval ships. The rational design of the passage could increase the efficiency of combat logistics, life-cycle logistics and personnel movement under combat and emergency conditions. Borrowing ideas from facility layout, the model of optimal vertical passage layout was established to formulation the vertical passage layout problem. The Floyd’s algorithm was used to calculate the distance between two compartments and the particle swarm optimization algorithm was proposed to solve the mathematical model. From simulation results, the model and methods are feasible for the vertical passage layout problem.

A Method of Parameters Selecting in Robust Control Based on PSO Algorithm

2011 ◽  
Vol 320 ◽  
pp. 574-579
Author(s):  
Hua Li ◽  
Zhi Cheng Xu ◽  
Shu Qing Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Robust Control ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Satisfactory Performance ◽  
Novel Method ◽  
Simulation Results

Aiming at a kind of uncertainties of models in complex industry processes, a novel method for selecting robust parameters is stated in the paper. Based on the analysis, parameters selecting for robust control is reduced to be an object optimization problem, and the particle swarm optimization (PSO) is used for solving the problem, and the corresponding robust parameters are obtained. Simulation results show that the robust parameters designed by this method have good robustness and satisfactory performance.

Quantum-Behaved Particle Swarm Optimization Algorithm for Solving Nonlinear Equations

2013 ◽  
Vol 756-759 ◽  
pp. 2926-2931 ◽  
Author(s):  
Xiao Feng Zhang ◽  
Gui Fang Sui
Keyword(s):  
Particle Swarm Optimization ◽  
Real Number ◽  
Nonlinear Equations ◽  
Optimization Algorithm ◽  
Traditional Method ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Simulation Results

A quantum-behaved particle swarm optimization algorithm is presented in this paper for solving nonlinear equations. The positions of particle are coded by probability amplitudes of qubits that are updated by quantum rotation gates in this method. The corresponding real number solution at specified interval can be extracted by this algorithm for solving nonlinear equations. Compared to real traditional method, the simulation results show that this algorithm is more accurate and effective.

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