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Connor, A M
Languages: English
Types: Doctoral thesis
Subjects: TJ
This thesis presents a novel design methodology for the synthesis of hybrid mechanisms using Genetic Algorithms. GAs are a search and optimisation method which model the mechanics of population genetics to give a truly global search method. In parallel to the development of a suitable GA, the work also develops novel objective function criteria which go some way to providing an approximation to dynamic criteria whilst using only kinematic properties during calculations. This has considerable effect in reducing the time required to find a feasible solution. The thesis presents a set of results which validate the proposed methodology, both in terms of speed of convergence and quality of the final solutions obtained. The application chosen is the synthesis of a hybrid five bar path generating mechanism. A description is given of the development of a practical machine for a given test case, so as to illustrate that the solutions produced are feasible in terms of real world implementation. Results are presented which show the effectiveness of the machine. Finally, a critical analysis of both the methodology and the results is carried out. This highlights some areas in which the methodology could be improved by future work.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 2 Literature Review 2.1 Introduction 2.2 Mechanism Design, Analysis and Synthesis 2.2.1 MechanismDesign : The Traditional Approach 2.2.2 AutomatedType Synthesisof Mechanisms 2.2.3 DimensionalSynthesisof Mechanisms 2.2.4 Additional Objectivesfor MechanismDesign 2.2.5 Multi-Degree of FreedomMechanisms 2.3 Optimisation Techniques 2.3.1 ClassicalOptimisation Theory 2.3.2 LinearProgramming 2.3.3 Non-Linear Programming 2.3.4 Simulation and Heuristic Methods SimulatedAnnealing The GreatDeluge Algorithm Flexible PolyhedronSearch Tabu Search Artificial Neural Networks Genetic Algorithms 2.4 Comparisonof Novel Optimisation Techniques 2.5 Applicationsof GeneticAlgorithms 2.6 Improvementtso the SimpleGeneticAlgorithm 2.6.1 Preventionof PrematureConvergencein GeneticAlgorithms 2.6.2 Improved GeneticOperatorsand SearchStrategies 2.6.3 Reductionof Bias in GeneticAlgorithms 2.7 Summary
    • 3 The Fundamentals of Genetic Algorithms 3.1 Introduction 3.2 Genetic Algorithms 3.2.1 The DifferencesBetweenGeneticAlgorithms andOther Search Methods 3.2.2 SolutionCoding, SchemataG,eneticOperatorsandFitness Solution Coding GeneticOperators An Introduction to Schemata FitnessFunction 3.3 A Basic Genetic Algorithm 3.3.1 A Hand Worked Genetic Algorithm Solution 3.4 The SchemaTheorem 3.4.1 SchemaOrder andDefining Length 3.4.2 MathematicalFormulation of the SchemaTheorem 3.4.3 Implications of the Schema Theorem TheBuilding Block Hypothesis Schemata as Hyperplanes Implicit Parallelism 6.3 Practical Implementation of a Real Machine 6.3.1 Commissioning 6.3.2 Control 6.3.3 MachineEvaluation 6.4 Summary
    • [121] Zeigler, J.G. & Nichols, N. B. (1942) Optimal Settingsfor Automatic Controllers Transactionosf theASME,Vol 68,Part8,pp759-768
    • [122] De Paor,A.M. (1993) A Fiftieth Anniversary Celebration of the Zeigler-Nichols PID Controller InternationalJournalof Electrical EngineeringEducation,Vol 30, No 4
    • [123] Donne,M.S., Tilley, D.G. & Richards,W. (1995) The Useof Multi-Objective Parallel GeneticAlgorithms to Aid Fluid Power System Design Proceedingsof the Institution of MechanicalEngineers,Part I, Vol 209, No 1,Journalof SystemsandControl Engineering,pp 53-61 [126] Johnson, J. & Piction, P. (1995) Mechatronics: DesigningIntelligent Machines Volume 11:Concepts in Artificial Intelligence Butterworth-Heineman if(oldpop[i]. ++tempscore;
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