Dissertation > Excellent graduate degree dissertation topics show

Studies on Individual Particle Swarm Optimization

Author: CaiXingJuan
Tutor: TanZuoï¼›ZengJianChao
School: Taiyuan University of Science and Technology
Course: Computer Software and Theory
Keywords: Particle Swarm Optimization Inertia weight Cognitive factor Social factors Algorithm structure
CLC: TP301.6
Type: Master's thesis
Year: 2008
Downloads: 64
Quote: 3
Read: Download Dissertation

Abstract


The particle swarm algorithm is a simulation of the social behavior of birds flying fish swimming groups of organisms groups stochastic optimization algorithm, due to its simple structure, fast operating speed, has been widely used in many fields. The papers from the agent (Agent) point of view, this paper proposes a personalized Particle Swarm Optimization framework and applied to parameter selection and structure optimization. Standard particle swarm algorithm using only the particles of memory, not consider particles other features. This limitation so that there is a big difference between the particle swarm algorithm and its biological background, thus affecting the efficiency of the algorithm. In view of this, the paper particles algorithm as memory capacity, communication ability, responsiveness, collaboration capabilities and self-learning ability of the agent (Agent) particles, personalized Particle Swarm Optimization framework. The algorithm on the basis of the standard particle swarm optimization, the use of multi-agent interaction between competition and mutual cooperation, so that the particles can better adapt to the surrounding environment, thus more in line with the biological background of the algorithm. The parameter selection is an important element of the particle swarm optimization to select different strategies, personalized parameter selection strategy needs to take full advantage of the communication of the particles with the existing parameters, response, collaboration and self-learning ability, resulting in different particles in the same generation in the parameter has a different value. Paper the pros and cons of the various particles on the ability to adapt to the environment the proposed linear performance evaluation as self-learning ability of the particles, and collaboration capabilities to dynamically adjust the ratio between the global search ability and local search ability. Based on this idea, we have successfully proposed the inertia weight, cognitive factors and social factors personalized selection strategy, simulation results show that these strategies can effectively improve the efficiency of the algorithm. For particle swarm optimization is another important research content - structure optimization, the paper according to the optimum position near global extreme point greater probability of this principle, discussed implementation of personalized particle swarm algorithm structure. Personalized inertia weight strategy with high selective pressure, it is easy to fall into local optimum. Accordingly, the paper introducing a special structure to limit the local searching ability to enhance the global search capability, thereby effectively avoiding the occurrence of the phenomenon of premature convergence. However, the strategy of the global search performance is still weak, the paper further proposed an evolutionary divergence. Simulation results show that the algorithm can effectively improve the diversity of the population.

Related Dissertations

  1. Research on Feature Extraction and Classification of Tongue Shape and Tooth-Marked Tongue in TCM Tongue Diagnosis,TP391.41
  2. Computing Minimum Distance between Curves/Surfaces Based on PSO Algorithm,O182
  3. Active Power Filter and Its Application in Distribution Network,TN713.8
  4. Research on Fuzzy C-Mean Clustering Algorithm Based on Particle Swarm Optimization and Shuffled Frog Leaping Algorithm,TP18
  5. Research on the Improvements and Applications of Particle Swarm Optimization,TP18
  6. Segmentation of cDNA Microarray Image Using Fuzzy C-means Algorithm Optimized by Particle Swarm,TP391.41
  7. Research on Modification and Application of Particle Swarm Optimization Algorithm Based on Control Methods,TP301.6
  8. Based on RFID Prison Intelligent Management System Research and Implementation,TP315
  9. Research of Path Planning for Small-Size Intelligent Soccer Robot in Complex and Dynamic Environment,TP242
  10. The Research on the Target Localization and Tracking Based on WSN,TN929.5
  11. Research on Mobile Robot Path Planning and Simulation Realization,TP242
  12. Research of Routing Technology for Wireless Sensor Network Based on Particle Swarm Optimization,TP212.9
  13. Research on Quantum Particle Swarm Optimization and Its Application to Codebook Design of Image Vector Quantization,TP301.6
  14. Research of Variable Neighborhood Search and Application in Combinatorial Optimization,TP301.6
  15. Research on Cultural Algorithm and Its Application in Constrained Optimization Problems,O224
  16. In-furnace Temperature Information Included Combustion Optimization of a Utility Boiler,TK227.1
  17. The Irreversibility Analysis of ECG and Fetal ECG Extraction,TN911.7
  18. The Modified Harmony Search Algorithm with Control Parameters Co-evolution and Its Application,TP391.3
  19. Cooperative Optimization Scheduling with Application to Multi-Reservoir System During Non-Flood Period,TV697.11
  20. Research on Multi-time Period Production and Procurement Plan of Supply Chain under Uncertainty,F224
  21. Multi-step-ahead Stock Price Index Forecasting Based on Hybrid Models,F224

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > General issues > Theories, methods > Algorithm Theory
© 2012 www.DissertationTopic.Net  Mobile