Dissertation > Excellent graduate degree dissertation topics show

Improved Particle Swarm Optimization Algorithm

Author: JiangTao
Tutor: LvXianRui
School: Jilin University
Course: Applied Mathematics
Keywords: optimization theory Particle Swarm Optimization Algorithm Improved Orthogonal
Type: Master's thesis
Year: 2013
Downloads: 125
Quote: 0
Read: Download Dissertation


With the development of modern society, both in scientific research and engineeringapplications, there are a variety of optimization problems, such as portfolio investment, fi-nancial analysis, signal processing, fitting function. Therefore, optimization theory and algo-rithm is widely concerned and constantly exploring the subject. The optimization problemis nonlinear, nonsmooth, high dimensional, complex problem is not continuous, the tradi-tional derivative based algorithm in solving the optimization problem is subject to certainrestrictions, in order to solve these problems, we propose stochastic optimization methods,such as ant colony algorithm, artificial fish-swarm algorithm, artificial bee colony algorith-m, bacterial foraging algorithm, particle swarm optimization algorithm of swarm intelligenceoptimization algorithm. Swarm intelligence computation using bionics theory, by using com-puter reconstruction and Simulation of biological characteristics, such as biological habits,adaptive behavior, the compiler design optimization algorithm. Optimization of practicalproblems based on algorithm. Bionic algorithm optimization problem is now widely accept-ed and successfully solved already emerge in an endless stream, these algorithms becomesolving engineering, network optimization, an efective approach to complex issues such asintelligent control.Since the proposed particle swarm algorithm, the people proposed many improvedmethods.Clerc[1] proposed the shrinkage factorχ,By introducing a shrinkage factor to con-trol parametersω,c1,c2.Riget and Vesterstorm[2]proposed a kind of attracting difusion parti-cle swarm optimization algorithm.The algorithm introduces two operator”attract”and”difus-ion”,it improved the efciency of the algorithm.Mendes and Kennedy put forward FIP-SO[3],the limit position of particles change weighted combination for all particles.ZengJianchao and Wang Lifang proposed generalized particle swarm optimization algorithm, ex-tended the particle swarm algorithm.Monson and Seppi[4]proposed Kalman particle swarmalgorithm.Zhang Xuanping§Du Yuping§Qin Guoqiang proposed changed inertia weightadaptive particle swarm optimization algorithm[5].In addition,mixed strategies of diferen-tial evolution algorithm and particle swarm optimization,mixed strategies of chaotic searchand particle swarm algorithm can improve the algorithm abilities. 2011,Shi etc.proposed brain storming optimization algorithm[7],it is a new optimizationalgorithm inspired by traditional brain storming methods.The traditional brain storming col-lected all people.s ideas,but it was disturbed easily by other persons.s ideas.Therefore,brainstorming optimization algorithm introduced the group,it improved the algorithm.s perfor-mance.2012§Zhan etc.proposed modified brain storming optimization algorithm[8],it trans-formed K-means to SGM strategy.Then,it improved the convergent speed.Through carefully studying of the algorithms, we propose a modified particle swarmoptimization algorithm based on orthogonal design, it is an optimization algorithm by us-ing bionic optimization ideas and simulating human thinking mode.We used the modifiedparticle swarm optimization algorithm and other three bionic optimization algorithms to dothe experiments by typical test functions,learning from the chart,the modified particle swarmalgorithm had better stability and global search ability.Through the image observation,theconvergence speed of the modified algorithm is fast. Therefore, the algorithm had strongrobustness,high efciency and high precision of search.

Related Dissertations

  1. Active Power Filter and Its Application in Distribution Network,TN713.8
  2. Research on Fuzzy C-Mean Clustering Algorithm Based on Particle Swarm Optimization and Shuffled Frog Leaping Algorithm,TP18
  3. Several types of algorithm and its application in non- cooperative games optimal solution,O224
  4. Quasi-Monte Carlo Method for the Structured Stochastic Variational Inequalities,O22
  5. Research on Cultural Algorithm and Its Application in Constrained Optimization Problems,O224
  6. The Study of Light and Water-Energy Co-Generation of Complex,TM61
  7. Research on Method of the Beam Bridge Structure Vibration Damage Identification,U441.4
  8. Communication constraints quantitative estimation system design and analysis,TP273
  9. Research of Internet Congestion Control Mechanism and Stability Analysis,TP393.06
  10. Study on the FCCU Risk Assessment of Sinopec Xi’an Branch,TE624.41
  11. Study on Outburst Prevention Mechanism and Injection Parameters Optimization of Hydraulic Extrusion,TD713
  12. The Study on Load Curtailment Optimization in Urban Electric Network,TM715
  13. Workflow system task scheduling strategy,TP311.52
  14. Closed-loop supply chain pricing model,F274
  15. Developing Second Best Institutions: A Competition Policy That Fits China’s Mode of Development,D922.294
  16. A Study on the Optimal Structure of Insurance Investment in China,F842
  17. CDN content distribution network optimization Method,TP393.02
  18. Swarm intelligence optimization algorithm in the path planning,O221
  19. Study on Drawing Forming Process of High-strength Steel Based on Controllable Drawbead,TG386
  20. Model Updating Research of a Test Cable-stayed Bridge,U448.27
  21. A Study on Value Enhancement of Urban Residential Communities Based on Optimization Theory,F293.35

CLC: > Industrial Technology > Automation technology,computer technology > Automated basic theory > Artificial intelligence theory
© 2012 www.DissertationTopic.Net  Mobile