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A modified particle swarm optimization and its application in thermal management of an electronic cooling system

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Title: A modified particle swarm optimization and its application in thermal management of an electronic cooling system
Author: Alrasheed, Mohammed R.A.
Degree: Doctor of Philosophy - PhD
Program: Mechanical Engineering
Copyright Date: 2011
Issue Date: 2011-10-12
Publisher University of British Columbia
Abstract: Particle Swarm Optimization (PSO) is an evolutionary computation technique, which has been inspired by the group behavior of animals such as schools of fish and flocks of birds. It has shown its effectiveness as an efficient, fast and simple method of optimization. The applicability of PSO in the design optimization of heat sinks is studied in this thesis. The results show that the PSO is an appropriate optimization tool for use in heat sink design.PSO has common problems that other evolutionary methods suffer from. For example, in some cases premature convergence can occur where particles tend to be trapped at local optima and not able to escape in seeking the global optimum. To overcome these problems, some modifications are suggested and evaluated in the present work. These modifications are found to improve the convergence rate and to enhance the robustness of the method. The specific modifications developed for PSO and evaluated in the thesis are: (1) Chaotic Acceleration Factor (2) Chaotic Inertia Factor (3) Global Best Mutation The performance of these modifications is tested through benchmarks problems, which are commonly found and used in the optimization literature. Detailed comparative analysis of the modifications to the classical PSO approach is made, which demonstrates the potential performance improvements. In particular, the modified PSO algorithms are applied to problems with nonlinear constraints. The non-stationary, multi-stage penalty method (PFM) is implemented to handle nonlinear constraints. Pressure vessel optimization and welded beam optimization are two common engineering problems that are used for testing the performance of optimization algorithms and are used here as benchmark testing examples. It is found that the modified PSO algorithms, as developed in this work, outperform many classical and evolutionary optimization algorithms in solving nonlinear constraint problems. The modified PSO algorithm is applied in heat sink design and detailed results are presented. The commercially available software package Ansys Icepak is used in the present work to solve the heat and flow equations in implementing the optimal design variables resulting from the modified PSO algorithms. The main contributions the work are summarized and suggestions are made for possible future work.
Affiliation: Applied Science, Faculty of
URI: http://hdl.handle.net/2429/37900
Scholarly Level: Graduate

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