Dissertation > Excellent graduate degree dissertation topics show

Chaotic Monkey Algorithm and Its Applications

Author: HaoShiPeng
Tutor: TangWanSheng
School: Tianjin University
Course: Systems Engineering
Keywords: Monkey Algorithm Chaotic Monkey Algorithm Fuzzy ConstraintSatisfaction Problem Evolutionary Algorithm Test functions
CLC: TP18
Type: Master's thesis
Year: 2010
Downloads: 138
Quote: 2
Read: Download Dissertation

Abstract


Intelligent algorithm have been studied for a long time, and a lot of goodintelligence search algorithms have been introduced, including genetic algorithms(GA), particle swarm optimization (PSO) and artificial neural network (ANN)and so on. These classic intelligent algorithm substantially increase the humanability to solve complex problems, and they have been widely applied in a varietyof engineering projects. However, because almost all of these classical algorithmshard to escape the“curse of dimension”, there still exit some restrictions whenputting them into implement.This thesis first introduces a new global optimization intelligent algorithm—monkey algorithm (MA). MA simulates the whole mountain climbing process ofmonkeys in nature, and designs three processes, namely climbing, watching, andjumping, to search for the global optimal solutions to continuous optimizationproblems. The algorithm is characterized by its insensitiveness to dimension ofthe optimization problem. The testing results of 11 functions show MA has theability to solve large-scale, multi-peak optimization problems, and enjoys quickspeed and high accuracy.Chaotic search, based on chaotic evaluation of variables, enjoys certainty, er-godicity and stochastic property, and has shown enormous ability of local search.In this thesis, a Chaotic Monkey Algorithm (CMA), which combines MA withchaotic search, is proposed. A set of sixteen well-known test functions is solvedwith CMA, and comparisons with MA, GA and PSO are made which show thatCMA can e?ectively enhance the searching effciency and greatly improve thesearching quality. Last but never least, the dimension test shows that CMA isinsensitive with the dimension of problems.In the end, CMA was used to solve a new kind of fuzzy constraint satisfac-tion problem. This thesis studies a kind of fuzzy constraint satisfaction problem(FCSP), in which the parameters with uncertainty are represented as fuzzy vari-ables. Using the credibility measure metrics the possibility of constraints, taking the joint credibility of all constraints as objective function, we transform theFCSP into an unconstrained optimization problem. A fuzzy simulation is usedto estimate the credibilities of the fuzzy events in the FCSP, based on which annew monkey algorithm is designed to solve the FCSP. Finally, several examplesare provided to illustrate the feasibility and the e?ectiveness of the proposedalgorithm.

Related Dissertations

  1. Evolutionary Clustering Algorithm and Its Application,TP311.13
  2. A Study on Dynamic Equivalence of Wind Farms,TM614
  3. Nonlinear evolution of the research and application of adaptive filtering and evolution of the array antenna signal,TN820.15
  4. Mind Evolutionary Algorithm and Its Applications in Microstrip Antenna Designing,TN820
  5. The Application of Improved Mind Evolutionary Algorithm in Antenna Array Synthesis,TN820
  6. Multi-agent evolutionary algorithm optimal load distribution in the thermal power plant application,TM621
  7. Knowledge Evoluation Algorithm and Its Application in Chemical Dynamic Optimization,TQ021.8
  8. Research on Path Planning for Mobile Robot Based on Evolutionary Algorithms,TP242
  9. Collaborative co- evolutionary algorithm and its application,TP18
  10. Research of Optimization of Slab Loading Problem,TP18
  11. The Embedded Attitude Measurement System’s Design,P228.4
  12. Based on multi-objective optimization evolutionary computation algorithms and applications,TP301.6
  13. Investigation and Application of Quantum Evolutionary Algorithm,TP301.6
  14. Last Revised bullet gesture analysis and multi-objective optimization design,TJ413.3
  15. Research and Application on Evolutionary Multi-Objective Algorithm Based on Arena Principle and Niche,O221.6
  16. The Grid Mechanism for Multi-Objective Evolutionary Algorithm,TP301.6
  17. Software reliability model and its parameter estimation,TP311.52
  18. Research on Evolutionary Algorithm for Attribute Reduction under Rough Set Model,TP301.67.231
  19. Evolutionary Algorithms for Traveling Salesman Problems,TP301.6
  20. Evolutionary Algorithms for Unconstrained Global Optimization Problems with Continuous Variables,TP18
  21. Study on Multi-Objective Optimization of Engineering Project Based on Co-evolutionary Algorithms,F284

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