Analysis of Standard Particle Swarm Optimization Algorithm Based on Markov Chain

2014 ◽  
Vol 39 (4) ◽  
pp. 381-389 ◽  
Author(s):  
Feng PAN ◽  
Qian ZHOU ◽  
Wei-Xing LI ◽  
Qi GAO
Keyword(s):  
Particle Swarm Optimization ◽  
Markov Chain ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Standard Particle Swarm Optimization

A Novel Particle Swarm Optimization with Random Parameters

2013 ◽  
Vol 631-632 ◽  
pp. 1324-1329
Author(s):  
Shao Rong Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Iteration Process ◽  
Local Optimum ◽  
Random Parameters ◽  
Swarm Optimization ◽  
Standard Particle Swarm Optimization ◽  
Fast Convergence Rate

To improve the performance of standard particle swarm optimization algorithm that is easily trapped in local optimum, based on analyzing and comparing with all kinds of algorithm parameter settings strategy, this paper proposed a novel particle swarm optimization algorithm which the inertia weight (ω) and acceleration coefficients (c1 and c2) are generated as random numbers within a certain range in each iteration process. The experimental results show that the new method is valid with a high precision and a fast convergence rate.

Target Position Measurement Technology Based on Quantum-Behaved Particle Swarm Optimization

2013 ◽  
Vol 303-306 ◽  
pp. 403-406 ◽  
Author(s):  
Jin Jie Yao ◽  
Jing Yang ◽  
Jian Li ◽  
Li Ming Wang ◽  
Yan Han
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Target Position ◽  
Measurement Technology ◽  
Position Measurement ◽  
Swarm Optimization ◽  
Standard Particle Swarm Optimization ◽  
Fitness Value

Quantum-behaved particle swarm optimization algorithm (QPSO) was proposed as a kind of swarm intelligence, which outperformed standard particle swarm optimization algorithm (PSO) in search ability. This paper presents an improved QPSO with nonlinear controlled parameter according to the fitness value of the particles. Simultaneously, we apply the improved QPSO to solve the problems of target position measurement. The experimental results show that the improved QPSO has faster convergence speed, higher measurement accuracy, and good localization performance.

Study of Particle Swarm Inertia Weight Adjustment Method Based on Fitness Changes after Multi-Step Iteration

2015 ◽  
Vol 740 ◽  
pp. 696-701
Author(s):  
Shu Hui Zheng ◽  
Ling Yu Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Adjustment Problems ◽  
Swarm Optimization ◽  
Adjustment Method ◽  
Inertia Weight ◽  
Standard Particle Swarm Optimization ◽  
Optimization Simulation

Considering the inertia weight adjustment problems in the standard particle swarm optimization algorithm, a kind of particle swarm inertia weight adjustment method based on multi-step iteration fitness changes was put forward, and by analyzing if particle optimal fitness values was further optimized after a certain number of iterations, then how to set the inertia weight was determined, which can balance the particle swarm global optimization and local optimization. Simulation results show that the improved algorithm was better than the standard particle swarm optimization algorithm in convergence speed and accuracy of solution.

Sign in / Sign up

Export Citation Format

Share Document