Critical analysis of angle modulated particle swarm optimisers

This dissertation presents an analysis of the angle modulated particle swarm optimisation (AMPSO) algorithm. AMPSO is a technique that enables one to solve binary optimisation problems with particle swarm optimisation (PSO), without any modifications to the PSO algorithm. While AMPSO has been successfully applied to a range of optimisation problems, there is little to no understanding of how and why the algorithm might fail. The work presented here includes in-depth theoretical and emprical analyses of the AMPSO algorithm in an attempt to understand it better. Where problems are identified, they are supported by theoretical and/or empirical evidence. Furthermore, suggestions are made as to how the identified issues could be overcome. In particular, the generating function is identified as the main cause for concern. The generating function in AMPSO is responsible for generating binary solutions. However, it is shown that the increasing frequency of the generating function hinders the algorithm’s ability to effectively exploit the search space. The problem is addressed by introducing methods to construct different generating functions, and to quantify the quality of arbitrary generating functions. In addition to this, a number of other problems are identified and addressed in various ways. The work concludes with an empirical analysis that aims to identify which of the various suggestions made throughout this dissertatioin hold substantial promise for further research.Dissertation (MSc)--University of Pretoria, 2017.Computer ScienceMScUnrestricte

Angle modulated population based algorithms to solve binary problems

Recently, continuous-valued optimization problems have received a great amount of focus, resulting in optimization algorithms which are very efficient within the continuous-valued space. Many optimization problems are, however, defined within the binary-valued problem space. These continuous-valued optimization algorithms can not operate directly on a binary-valued problem representation, without algorithm adaptations because the mathematics used within these algorithms generally fails within a binary problem space. Unfortunately, such adaptations may alter the behavior of the algorithm, potentially degrading the performance of the original continuous-valued optimization algorithm. Additionally, binary representations present complications with respect to increasing problem dimensionality, interdependencies between dimensions, and a loss of precision. This research investigates the possibility of applying continuous-valued optimization algorithms to solve binary-valued problems, without requiring algorithm adaptation. This is achieved through the application of a mapping technique, known as angle modulation. Angle modulation effectively addresses most of the problems associated with the use of a binary representation by abstracting a binary problem into a four-dimensional continuous-valued space, from which a binary solution is then obtained. The abstraction is obtained as a bit-generating function produced by a continuous-valued algorithm. A binary solution is then obtained by sampling the bit-generating function. This thesis proposes a number of population-based angle-modulated continuous-valued algorithms to solve binary-valued problems. These algorithms are then compared to binary algorithm counterparts, using a suite of benchmark functions. Empirical analysis will show that the angle-modulated continuous-valued algorithms are viable alternatives to binary optimization algorithms. Copyright 2012, University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. Please cite as follows: Pamparà, G 2012, Angle modulated population based algorithms to solve binary problems, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd C12/4/188/gmDissertation (MSc)--University of Pretoria, 2012.Computer Scienceunrestricte

Using SetPSO to determine RNA secondary structure

RNA secondary structure prediction is an important field in Bioinformatics. A number of different approaches have been developed to simplify the determination of RNA molecule structures. RNA is a nucleic acid found in living organisms which fulfils a number of important roles in living cells. Knowledge of its structure is crucial in the understanding of its function. Determining RNA secondary structure computationally, rather than by physical means, has the advantage of being a quicker and cheaper method. This dissertation introduces a new Set-based Particle Swarm Optimisation algorithm, known as SetPSO for short, to optimise the structure of an RNA molecule, using an advanced thermodynamic model. Structure prediction is modelled as an energy minimisation problem. Particle swarm optimisation is a simple but effective stochastic optimisation technique developed by Kennedy and Eberhart. This simple technique was adapted to work with variable length particles which consist of a set of elements rather than a vector of real numbers. The effectiveness of this structure prediction approach was compared to that of a dynamic programming algorithm called mfold. It was found that SetPSO can be used as a combinatorial optimisation technique which can be applied to the problem of RNA secondary structure prediction. This research also included an investigation into the behaviour of the new SetPSO optimisation algorithm. Further study needs to be conducted to evaluate the performance of SetPSO on different combinatorial and set-based optimisation problems.Dissertation (MS)--University of Pretoria, 2009.Computer Scienceunrestricte

A review of asset management literature on multi-asset systems

This article gives an overview of the literature on asset management for multi-unit systems with an emphasis on two multi-asset categories: fleet (a system of homogeneous assets) and portfolio (a system of heterogeneous assets). As asset systems become more complicated, researchers have employed different terms to refer to their specific problems. With an objective to facilitate readers in searching conducive studies to their interests, this paper establishes a novel classification scheme for multi-unit systems in accordance with essential features such as diversity of assets and intervention options. Moreover, discerning differences in characteristics between cross-component and cross-asset interactions, we select three types of potential multi-component dependencies (performance, stochastic, and resource) and extend their notions to be applicable to multi-asset systems. The investigation into these dependencies enables the identification of problems that could exist in real industrial settings but are yet to be determined in academia. Ultimately, we delve into modelling approaches adopted by previous researchers. This comprehensive information allows us to offer the insights into the current trends in multi-asset maintenance. We expect that the output of this review paper will not only stress research gaps on multi-asset systems, but more importantly help systematise future studies on this aspect

Parallelised and vectorised ant colony optimization

Ant Colony Optimisation (ACO) is a versatile population-based optimisation metaheuristic based on the foraging behaviour of certain species of ant, and is part of the Evolutionary Computation family of algorithms. While ACO generally provides good quality solutions to the problems it is applied to, two key limitations prevent it from being truly viable on large-scale problems: A high memory requirement that grows quadratically with instance size, and high execution time. This thesis presents a parallelised and vectorised implementation of ACO using OpenMP and AVX SIMD instructions; while this alone is enough to improve upon the execution time of the algorithm, this implementation also features an alternative memory structure and a novel candidate set approach, the use of which significantly reduces the memory requirement of ACO. This parallelism is enabled through the use of Max-Min Ant System, an ACO variant that only utilises local memory during the solution process and therefore risks no synchronisation issues, and an adaptation of vRoulette, a vector-compatible variant of the common roulette wheel selection method. Through the use of these techniques ACO is also able to find good quality solutions for the very large Art TSPs, a problem set that has traditionally been unfeasible to solve with ACO due to high memory requirements and execution time. These techniques can also benefit ACO when it comes to solving other problems. In this case the Virtual Machine Placement problem, in which Virtual Machines have to be efficiently allocated to Physical Machines in a cloud environment, is used as a benchmark, with significant improvements to execution time

2020 GREAT Day Program

SUNY Geneseo’s Fourteenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1014/thumbnail.jp

2019 GREAT Day Program

SUNY Geneseo’s Thirteenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1013/thumbnail.jp

Dynamic optimisation for energy efficiency of injection moulding process

Low carbon economy has emerged as an important task in China since the energy intensity and carbon intensity reduction targets were clearly prescribed in its recent Twelfth Five-Year Plan during 2011-2015. While the largest enterprises have undertaken initial initiative to reduce their energy consumption, small and medium-sized enterprises (SMEs) will need to share the responsibilities in meeting the nation’s targets. However, there is no established structure for helping SMEs to reduce their efficiency gap and hence the implementation of energy-saving measures in SMEs still remains patchy. Addressing this issue, this thesis seeks to understand the critical barriers faced by SMEs and aims to develop proprietary methodologies that can facilitate manufacturing SMEs to close their efficiency gap. Process parameters optimisation is perceived to be an effective “no-cost” strategy which can be conducted by SMEs to realise energy efficiency improvement. A unique design of experiment (DOE) introduced by Dorian Shainin offers a simplistic framework to study process optimisation, but its application is not widespread and being criticised over its working principles. In order to address the inherent limitations of the Shainin’s method, it was integrated with the multivariate statistical methods and the signal-response system in the empirical study. The nature of the research aim also requires a theoretical approach to evaluate the economic performance of the energy efficiency investment. Hence, a spreadsheet-based decision support system (file SERP.xlsm) was created via dynamic programming (DP) method. The main contributions of this thesis can be subdivided into empirical level and theoretical level. At the empirical level, a technically feasible yet economically viable approach called “multi-response dynamic Shainin DOE” was developed. An empirical study on the injection moulding process was presented to examine the validity of this novel integrated methodology. The emphasis has been on the integration of multivariate techniques and signal-response analysis. The former successfully identified the critical factors to energy consumption and moulded parts’ impact performance regardless of the great fluctuation in the impact response. The latter enables the end-user to achieve different performance output based on the particular intent. At the theoretical level, the “DP-based spreadsheet solution” provides a convenient platform to help the rationally-behaved decision makers evaluate the energy efficiency investments. A simple hypothetical case study on the injection moulding industry was illustrated how the decision-making process for equipment replacement can evolve over time. To sum up, both proprietary methodologies enhance the dynamicity in the optimisation process to support injection moulding industry in closing their efficiency gap. The study at the empirical level was limited by the absence of real industrial case study where it is important to justify the practicality of the proposed methodology. Regarding the theoretical level, the dataset for initial validation on the spreadsheet solution was not available. Finally, it is important to continue the future work on the research limitations in order to increase the cogency of the proprietary methodologies for common use in the industry

Dynamic optimisation for energy efficiency of injection moulding process

Low carbon economy has emerged as an important task in China since the energy intensity and carbon intensity reduction targets were clearly prescribed in its recent Twelfth Five-Year Plan during 2011-2015. While the largest enterprises have undertaken initial initiative to reduce their energy consumption, small and medium-sized enterprises (SMEs) will need to share the responsibilities in meeting the nation’s targets. However, there is no established structure for helping SMEs to reduce their efficiency gap and hence the implementation of energy-saving measures in SMEs still remains patchy. Addressing this issue, this thesis seeks to understand the critical barriers faced by SMEs and aims to develop proprietary methodologies that can facilitate manufacturing SMEs to close their efficiency gap. Process parameters optimisation is perceived to be an effective “no-cost” strategy which can be conducted by SMEs to realise energy efficiency improvement. A unique design of experiment (DOE) introduced by Dorian Shainin offers a simplistic framework to study process optimisation, but its application is not widespread and being criticised over its working principles. In order to address the inherent limitations of the Shainin’s method, it was integrated with the multivariate statistical methods and the signal-response system in the empirical study. The nature of the research aim also requires a theoretical approach to evaluate the economic performance of the energy efficiency investment. Hence, a spreadsheet-based decision support system (file SERP.xlsm) was created via dynamic programming (DP) method. The main contributions of this thesis can be subdivided into empirical level and theoretical level. At the empirical level, a technically feasible yet economically viable approach called “multi-response dynamic Shainin DOE” was developed. An empirical study on the injection moulding process was presented to examine the validity of this novel integrated methodology. The emphasis has been on the integration of multivariate techniques and signal-response analysis. The former successfully identified the critical factors to energy consumption and moulded parts’ impact performance regardless of the great fluctuation in the impact response. The latter enables the end-user to achieve different performance output based on the particular intent. At the theoretical level, the “DP-based spreadsheet solution” provides a convenient platform to help the rationally-behaved decision makers evaluate the energy efficiency investments. A simple hypothetical case study on the injection moulding industry was illustrated how the decision-making process for equipment replacement can evolve over time. To sum up, both proprietary methodologies enhance the dynamicity in the optimisation process to support injection moulding industry in closing their efficiency gap. The study at the empirical level was limited by the absence of real industrial case study where it is important to justify the practicality of the proposed methodology. Regarding the theoretical level, the dataset for initial validation on the spreadsheet solution was not available. Finally, it is important to continue the future work on the research limitations in order to increase the cogency of the proprietary methodologies for common use in the industry

Modeling, Parameter Identification, and Degradation-Conscious Control of Polymer Electrolyte Membrane (PEM) Fuel Cells

Polymer electrolyte membrane (PEM) fuel cells are touted as zero-emission alternatives to internal combustion engines for automotive applications. However, high cost and durability issues have hindered their commercialization. Therefore, significant research efforts are underway to better understand the scientific aspects of PEM fuel cell operation and engineer its components for improved lifetime and reduced cost. Most of the research in this area has been focused on material development. However, as demonstrated by Toyota's fuel cell vehicle, intelligent control strategies may lead to significantly improved durability of the fuel cell stack even with existing materials. Therefore, it seems that the outstanding issues can be better resolved through a combination of improved materials and effective control strategies. Accordingly, this dissertation aims to develop a model-based control strategy to improve performance and durability of PEM fuel cell systems for automotive applications. To this end, the dissertation first develops a physics-based and computationally efficient model for online estimation purposes. The need for such a model arises from the fact that detailed information about the internal states of the cell is required to develop effective control strategies for improved performance and durability, and such information is rarely available from direct measurements. Therefore, a software sensor must be developed to provide the required signals for a control system. To this end, this work utilizes spatio-temporal decoupling of the underlying problem to develop a model that can estimate water and temperature distributions throughout an operating fuel cell in a computationally efficient manner. The model is shown to capture a variety of complex physical phenomena, while running at least an order of magnitude faster than real time for dynamically changing conditions. The model is also validated with extensive experimental measurements under different operating conditions that are of interest for automotive applications. Furthermore, the dissertation extensively explores the sensitivity of the model predictions to a variety of parameters. The sensitivity results are used to study the parameter identifiability problem in detail. The challenges associated with parameter identification in such a large-scale physics-based model are highlighted and a model parameterization framework is proposed to address them. The proposed framework consists of three main components: (1) selecting a subset of model parameters for identification, (2) optimally designing experiments that are maximally informative for parameter identification, and (3) designing a multi-step identification algorithm that ensures sufficient regularization of the inverse problem. These considerations are shown to lead to effective model parameterization with limited experimental measurements. Finally, the dissertation uses a version of the proposed model to develop a degradation-conscious model-predictive control (MPC) framework to enhance the performance and durability of PEM fuel cell systems. In particular, a reduced-order model is developed for control design, which is then successively linearized about the current operating point to enable use of linear control design techniques that offer significant computational advantages. A variety of constraints on system safety and durability are considered and simulation case studies are conducted to evaluate the framework's utility in maximizing performance while respecting the durability constraints. It is also shown that the linear MPC framework employed here can generate the optimal control commands faster than real time. Therefore, the proposed framework is expected to be implementable in practical applications and contribute to extending the lifetime of fuel cell systems.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155288/1/goshtasb_1.pd