Research on Route Planning of Cruise Missile Based on Improved Particle Swarm Optimization Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 1170-1175 ◽  
Author(s):  
Xiao Xiao Ma ◽  
Tao Li ◽  
Li Qing
Keyword(s):  
Particle Swarm Optimization Algorithm ◽  
Cost Model ◽  
Route Planning ◽  
Pso Algorithm ◽  
Cruise Missile ◽  
Planning Problem ◽  
Digital Maps ◽  
Route Cost ◽  
Better Than ◽  
Improved Algorithm

For the route planning problem of cruise missile, a method based on PSO is proposed and simulated. The route cost model is established which in-tegrates the terrain cost, the threating cost and the stratagem obviation cost. With the model, digital maps and battlefield information can be transformed to the grid space for route planning. For the premature defect in the standard PSO algorithm, a method based on similarity variation is proposed for route planning. Simulation results show that the searching effect of the improved algorithm is better than which of the standard algorithm in route planning, the improved algorithm can get better route.

ANALYSIS OF MUTATION OPERATORS ON QUANTUM-BEHAVED PARTICLE SWARM OPTIMIZATION ALGORITHM

2009 ◽  
Vol 05 (02) ◽  
pp. 487-496 ◽  
Author(s):  
WEI FANG ◽  
JUN SUN ◽  
WENBO XU
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Novel Mutation ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Mutation Operator ◽  
Fundamental Theory ◽  
Swarm Optimization ◽  
Mutation Operators ◽  
Better Than

Mutation operator is one of the mechanisms of evolutionary algorithms (EAs) and it can provide diversity in the search and help to explore the undiscovered search place. Quantum-behaved particle swarm optimization (QPSO), which is inspired by fundamental theory of PSO algorithm and quantum mechanics, is a novel stochastic searching technique and it may encounter local minima problem when solving multi-modal problems just as that in PSO. A novel mutation mechanism is proposed in this paper to enhance the global search ability of QPSO and a set of different mutation operators is introduced and implemented on the QPSO. Experiments are conducted on several well-known benchmark functions. Experimental results show that QPSO with some of the mutation operators is proven to be statistically significant better than the original QPSO.

scholarly journals Digital FIR Filter Design by PSO and its variants Attractive and Repulsive PSO(ARPSO) & Craziness based PSO(CRPSO)

2021 ◽  
Vol 9 (6) ◽  
pp. 136-141
Author(s):  
Zain Ali ◽  
Bharat Lal Harijan ◽  
Tayab Din Memon ◽  
Nazmus Naf ◽  
Ubed-u-Rahman Memon
Keyword(s):  
Video Processing ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Filter Design ◽  
Pso Algorithm ◽  
Search Space ◽  
Fir Filter ◽  
Unique Approach ◽  
Fir Filter Design ◽  
Better Than

Digital filters play a major role in signal processing that are employed in many applications such as in control systems, audio or video processing systems, noise reduction applications and different systems for communication. In this regard, FIR filters are employed because of frequency stability and linearity in their phase response. FIR filter design requires multi-modal optimization problems. Therefore, PSO (Particle Swarm Optimization) algorithm and its variants are more adaptable techniques based upon particles’ population in the search space and a great option for designing FIR filter. PSO and its different variants improve the solution characteristic by providing a unique approach for updating the velocity and position of the swarm. An optimized set of filter coefficient is produced by PSO and its variant algorithms which gives the optimized results in passband and stopband. In this research paper, Digital FIR filter is effectively designed by using PSO Algorithm and its two variants ARPSO and CRPSO in MATLAB. The outcomes prove that the filter design technique using CRPSO is better than filter design by PM algorithm. PSO and ARPSO Algorithms in the context of frequency spectrum and RMS error.

Route Planning of Geomagnetic Aided Navigation for Vehicle under Matching Suitability Constraints

2013 ◽  
Vol 454 ◽  
pp. 39-42
Author(s):  
Peng Wang ◽  
Xiao Ping Hu ◽  
Mei Ping Wu ◽  
Hua Mu ◽  
Hai Ping Yuan
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Route Planning ◽  
Pso Algorithm ◽  
Planning Method ◽  
Experimental Results ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Movement Distance

In geomagnetic aided navigation (GAN), the vehicle is expected to traverse the areas with excellent matching suitability in order to obtain high matching precision. The route planning problem under matching suitability constraints is studied based on particle swarm optimization (PSO) algorithm in this article. Firstly, the PSO algorithm is briefly introduced and the expanding space of route nodes is determined with the maneuverability constraints of the vehicle. Then the minimum movement distance, the ability of avoiding threats and the proximity to suitable-matching areas are considered to construct the fitness function of PSO algorithm. Further the route planning method under matching suitability constraints is proposed. Experimental results show that the proposed method is effective, and the vehicle can successfully avoid the threats and can traverse the suitable-matching areas.

Route planning of intelligent bridge cranes based on an improved artificial potential field method

2021 ◽  
pp. 1-8
Author(s):  
Zhengyan Chang ◽  
Zhengwei Zhang ◽  
Qiang Deng ◽  
Zheren Li
Keyword(s):  
Potential Field ◽  
Autonomous Navigation ◽  
Route Planning ◽  
Simple Algorithm ◽  
Field Method ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Operating Characteristics ◽  
Potential Field Method ◽  
Artificial Potential Field Method

The artificial potential field method is usually applied to the path planning problem of driverless cars or mobile robots. For example, it has been applied for the obstacle avoidance problem of intelligent cars and the autonomous navigation system of storage robots. However, there have been few studies on its application to intelligent bridge cranes. The artificial potential field method has the advantages of being a simple algorithm with short operation times. However, it is also prone to problems of unreachable targets and local minima. Based on the analysis of the operating characteristics of bridge cranes, a two-dimensional intelligent running environment model of a bridge crane was constructed in MATLAB. According to the basic theory of the artificial potential field method, the double-layer artificial potential field method was deduced, and the path and track fuzzy processing method was proposed. These two methods were implemented in MATLAB simulations. The results showed that the improved artificial potential field method could avoid static obstacles efficiently.

The Optimization of Project Time-Cost Based on Particle Swarm Optimization Algorithm

2013 ◽  
Vol 791-793 ◽  
pp. 1423-1426
Author(s):  
Hai Min Wei ◽  
Rong Guang Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Implementation Process ◽  
Pso Algorithm ◽  
Project Schedule ◽  
Swarm Optimization ◽  
Schedule Optimization ◽  
Schedule Management

Project schedule management is the management to each stage of the degree of progress and project final deadline in the project implementation process. Its purpose is to ensure that the project can meet the time constraints under the premise of achieving its overall objectives.When the progress of schedule found deviation in the process of schedule management ,the progress of the plan which have be advanced previously need to adjust.This article mainly discussed to solve the following two questions:establish the schedule optimization model by using the method of linear;discuss the particle swarm optimization (PSO) algorithm and its parameters which have effect on the algorithm:Particle swarm optimization (PSO) algorithm is presented in the time limited project and the application of a cost optimization.

Analysis of FBG Reflection Spectrum with PSO Algorithm

2012 ◽  
Vol 182-183 ◽  
pp. 1953-1957
Author(s):  
Zhao Xia Wu ◽  
Shu Qiang Chen ◽  
Jun Wei Wang ◽  
Li Fu Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
High Precision Measurement ◽  
Swarm Optimization ◽  
Fbg Sensor ◽  
Fast Convergence Rate ◽  
Force Sense

When the parameters were measured by using fiber Bragg grating (FBG) in practice, there were some parameters hard to measure, which would influenced the reflective spectral of FBG severely, and make the characteristic information harder to be extracted. Therefore, particle swarm optimization algorithm was proposed in analyzing the uniform force reflective spectral of FBG. Based on the uniform force sense theory of FBG and particle swarm optimization algorithm, the objective function were established, meanwhile the experiment and simulation were constructed. And the characteristic information in reflective spectrum of FBG was extracted. By using particle swarm optimization algorithm, experimental data showed that particle swarm optimization algorithm used in extracting the characteristic information not only was efficaciously and easily, but also had some advantages, such as high accuracy, stability and fast convergence rate. And it was useful in high precision measurement of FBG sensor.

scholarly journals A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
Ali R. Guner ◽  
Mehmet Sevkli
Keyword(s):  
Particle Swarm Optimization ◽  
Facility Location ◽  
Location Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Facility Location Problem ◽  
Discrete Version ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Better Than

A discrete version of particle swarm optimization (DPSO) is employed to solve uncapacitated facility location (UFL) problem which is one of the most widely studied in combinatorial optimization. In addition, a hybrid version with a local search is defined to get more efficient results. The results are compared with a continuous particle swarm optimization (CPSO) algorithm and two other metaheuristics studies, namely, genetic algorithm (GA) and evolutionary simulated annealing (ESA). To make a reasonable comparison, we applied to same benchmark suites that are collected from OR-library. In conclusion, the results showed that DPSO algorithm is slightly better than CPSO algorithm and competitive with GA and ESA.

A New Formulation for the Dial-a-Ride Problem

2021 ◽  
Author(s):  
Yannik Rist ◽  
Michael A. Forbes
Keyword(s):  
Time Window ◽  
Valid Inequalities ◽  
Route Planning ◽  
Branch And Cut ◽  
Mixed Integer ◽  
Planning Problem ◽  
Current State ◽  
New Formulation ◽  
First Time ◽  
Delivery Location

This paper proposes a new mixed integer programming formulation and branch and cut (BC) algorithm to solve the dial-a-ride problem (DARP). The DARP is a route-planning problem where several vehicles must serve a set of customers, each of which has a pickup and delivery location, and includes time window and ride time constraints. We develop “restricted fragments,” which are select segments of routes that can represent any DARP route. We show how to enumerate these restricted fragments and prove results on domination between them. The formulation we propose is solved with a BC algorithm, which includes new valid inequalities specific to our restricted fragment formulation. The algorithm is benchmarked on existing and new instances, solving nine existing instances to optimality for the first time. In comparison with current state-of-the-art methods, run times are reduced between one and two orders of magnitude on large instances.

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