Dissertation > Excellent graduate degree dissertation topics show

Improvement and Research Based on Bee Evolutionary Genetic Algorithm

Author: ZhaoQian
Tutor: XuWeiHong
School: Changsha University of Science and Technology
Course: Computer Software and Theory
Keywords: Genetic algorithm Bee evolutionary Random population Meta search engine Multi-objective optimization
CLC: TP18
Type: Master's thesis
Year: 2011
Downloads: 24
Quote: 0
Read: Download Dissertation

Abstract


Search engine solves the problem of rapid information retrieval. However, as the search methods difference, information retrieval efficiency and accuracy will be different. Meta search engine composites the advantages of each search engine, through the scheduling of each search engine, to get better effect. The current scheduling algorithms cannot coordinate the relationship among recall, precision and response time well.Genetic algorithm is a kind of common intelligence algorithm with widely application. Bee evolutionary genetic algorithm is an improved genetic algorithm based on bee genetic regularity. Since the random population size influents on the performance of the bee evolutionary genetic algorithm, this thesis puts forward some improvement to this algorithm. And it was applied to the multi-dimensions function optimization and meta search engines scheduling, and then obtained good results. The main research results are as follows:(1)Through the analysis of the bee evolutionary genetic algorithm, an improved bee evolutionary genetic algorithm was proposed; the algorithm uses the strategy by stages to adjust the dynamic random population scale. The random population size changing gradually, guarantees not only the diversity of population, but also improvement of convergence speed and accuracy for the algorithm. The experimental results by the optimizations of typical high dimensional function show that the algorithm is effective and feasible.(2)The improved bee evolutionary genetic algorithm will be applied in the scheduling of meta search engine, as well as dynamical optimization of many independent search engine. When Multi-objective combinatorial optimization scheduling cannot get the most optimal at the same time, the fitness of each objective function will coordinate the combination of each search engine optimization by adjusting individual comprehensive fitness weight, finally find the search engine combination of non-inferior solution scheduling list. The experimental results show that the proposed algorithm enhances the scheduling efficiency and is better than conventional technique in precision and speed.

Related Dissertations

  1. Development of the Platform for Compressor Optimization Design and Aerodynamic Optimization Design in the Transonic Compressor,TH45
  2. The Application of Fuzzy Comprehensive Evaluation Based on Genetic Algorithm in Vocational Evaluation of Classroom Teaching,G712
  3. Study on Taste Characteristic of Taste Peptide Enzymatic Production from Oyster Base on A Neural Network Method,TS254.4
  4. Design and Realization of the Magnetic Antenna in MW and SW Bands Based on Genetic Algorithm,TN820
  5. Citrus Image Segmentation Based on Genetic Algorithm,TP391.41
  6. Research of Scheduling Algorithm Based on Hybrid Adaptive Genetic Algorithm in Computing Grid,TP393.09
  7. Public Transport Optimal Dispatching Based on the Genetic-Newton Algorithm,TP18
  8. BP network optimization based on genetic algorithm optimization of the biodiesel process,TE667
  9. The Research on Texture Synthesis Technology from Cloud Theory & Been Evolution Genetic Algorithm,TP391.41
  10. Research on Clustering Algorithm Based on Genetic Algorithm and Rough Set Theory,TP18
  11. Research on Subsea Pipeline Repair Coupling,TE973
  12. Mining resources based on genetic algorithm optimization model of,O224
  13. The magnetorheological damper mechanical properties and Gun Recoil,TB535.1
  14. Optimization Study on Gating System and Molding Process Parameters of Injection Mold Based on Simulation,TQ320.662
  15. Research on the Milling Performance and Parameters Optimization with Large Parts of Heavy Machine,TG54
  16. Research of Adaptive Active Noise Control Based on Neural Network,TP183
  17. The Design and Implementation of Email Analysis and Forensies System,D918.2
  18. Sdesign and Implementation of Course Scheduling Management System,TP311.52
  19. Sentence Similarity Computing Research and Application of Intelligent Question Answering System,TP391.1
  20. The Study and Development of Production Planning and Management System for Small and Medium Discrete Enterprises,TP311.52
  21. Research on Feature Extraction, Selection and Classification Algorithms for Pulmonary CAD,TP391.41

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