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.

scholarly journals Research on Timetabling Problems Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 6-7 ◽  
pp. 736-741
Author(s):  
Xin Min Ma ◽  
Lin Li Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Educational System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Course Timetabling ◽  
Fitness Value ◽  
Course Timetabling Problem

A new algorithm for timetabling based on particle swarm optimization algorithm was proposed, and the key problems such as particle coding, fitness function fabricating, particle swarm initialization and crossover operation were settled. The fitness value declines when the evolution generation increases. The results showed that it was a good solution for course timetabling problem in the educational system.

scholarly journals Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm for Solving Optimal Reactive Power Dispatch Problem

2020 ◽  
Vol 3 (1) ◽  
pp. 12-21
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Symbiotic Organisms Search ◽  
Power Dispatch ◽  
Fitness Value ◽  
Symbiotic Organisms

In this work Hybridization of Genetic Particle Swarm Optimization Algorithm with Symbiotic Organisms Search Algorithm (HGPSOS) has been done for solving the power dispatch problem. Genetic particle swarm optimization problem has been hybridized with Symbiotic organisms search (SOS) algorithm to solve the problem. Genetic particle swarm optimization algorithm is formed by combining the Particle swarm optimization algorithm (PSO) with genetic algorithm (GA).  Symbiotic organisms search algorithm is based on the actions between two different organisms in the ecosystem- mutualism, commensalism and parasitism. Exploration process has been instigated capriciously and every organism specifies a solution with fitness value.  Projected HGPSOS algorithm improves the quality of the search.  Proposed HGPSOS algorithm is tested in IEEE 30, bus test system- power loss minimization, voltage deviation minimization and voltage stability enhancement has been attained.

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.

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.

An Adaptive Particle Swarm Optimization Algorithm Based on Cloud Model

2010 ◽  
Vol 129-131 ◽  
pp. 612-616
Author(s):  
Jin Rong Zhu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Cloud Model ◽  
Particle Swarm ◽  
Function Optimization ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Fitness Value ◽  
Model C

In this paper, an adaptive particle swarm optimization algorithm based on cloud model (C-APSO) is proposed. In the suggested method, the velocities of the all particles are adjusted based on the strategy that a particle whose fitness value is nearer to the optimal particle will fly with smaller velocity. Considering the properties of randomness and stable tendency of a normal cloud model, a Y-conditional normal cloud generator is used to gain the inertial factors of the particles. The simulations of function optimization show that the proposed method has advantage of global convergence property and can effectively alleviate the problem of premature convergence.

A Novel Hybrid Optimization Algorithm Based on Multi-Agent and Particle Swarm

2011 ◽  
Vol 460-461 ◽  
pp. 512-517
Author(s):  
De Jia Shi ◽  
Wei Jin Jiang ◽  
Xiao Ling Ding
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Agent Interactions ◽  
Fitness Value ◽  
Multi Agent

A novel multi-agent particle swarm optimization algorithm (MAI'SO) is proposed for optimal reactive power dispatch and voltage control of power system. The method integrates multi-agent system (MAS) and particle swarm optimization algorithm (PSO). An agent in MAI.SO represents a particle to PSO and a candidate solution to the optimization problem. All agents live in a lattice-like environment, with each agent fixed on a lattice-point. In order to decrease fitness value, quickly, agents compete and cooperate with their neighbors. and they can also use knowledge. Making use of these agent interactions and evolution mechanism of I.SO. MAPSO realizes the purpose of' minimizing the value of' objective function. MAPSO applied for optimal reactive power is evaluated on an IEEE 30-bus power system. It is shown that the proposed approach converges to better solutions much faster than the earlier reported approaches

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