The Multiple Cooling Multi-Objective Simulated Annealing Algorithm is used for the conceptual design optimization of a supersonic Unmanned Combat Aerial Vehicle (UCAV). Single and multiobjective optimization problems are addressed while limiting performance requirements between desired bounds to obtain viable aircraft configurations. A conceptual aircraft design code was prepared for planned but flexible combat missions. The results demonstrate that the optimization technique employed is an effective tool for the conceptual design of aircrafts.

Plug-in Hybrid Electric vehicle (PHEV) technology has the potential to reduce operational costs, greenhouse gas (GHG) emissions, and gasoline consumption in the transportation market. However, the net benefits of using a PHEV depend critically on several aspects, such as individual travel patterns, vehicle powertrain design and battery technology. To examine these effects, a multi-objective optimization model was developed integrating vehicle physics simulations through a Matlab/Simulink model, battery durability, and Canadian driving survey data. Moreover, all the drivetrains are controlled implicitly by the ADVISOR powertrain simulation and analysis tool. The simulated model identifies Pareto optimal vehicle powertrain configurations using a multi-objective Pareto front pursuing genetic algorithm by varying combinations of powertrain components and allocation of vehicles to consumers for the least operational cost, and powertrain cost under various driving assumptions. A sensitivity analysis over the foremost cost parameters is included in determining the robustness of the optimized solution of the simulated model in the presence of uncertainty. Here, a comparative study is also established between conventional and hybrid electric vehicles (HEVs) to PHEVs with equivalent optimized solutions, size and performance (similar to Toyota Prius) under both the urban and highway driving environments. In addition, breakeven point analysis is carried out that indicates PHEV lifecycle cost must fall within a few percent of CVs or HEVs to become both the environmentally friendly and cost-effective transportation solutions. Finally, PHEV classes (a platform with multiple powertrain architectures) are optimized taking into account consumer diversity over various classes of light-duty vehicle to investigate consumer-appropriate architectures and manufacturer opportunities for vehicle fleet development utilizing simplified techno-financial analysis. / Graduate / 0540 / 0548 / ehtesham@uvic.ca

Numerous advantages attained by integrating Distributed Generation (DG) in distribution systems. These advantages include decreasing power losses and improving voltage profiles. Such benefits can be achieved and enhanced if DGs are optimally sized and located in the systems. In this thesis, the optimal DG placement and sizing problem is investigated using two approaches. First, the optimization problem is treated as single-objective optimization problem, where the system’s active power losses are considered as the objective to be minimized. Secondly, the problem is tackled as a multi-objective one, focusing on DG installation costs. These problems are formulated as constrained nonlinear optimization problems using the Sequential Quadratic Programming method. A weighted sum method and a fuzzy decision-making method are presented to generate the Pareto optimal front and also to obtain the best compromise solution. Single and multiple DG installation cases are studied and compared to a case without DG, and a 15-bus radial distribution system and 33-bus meshed distribution system are used to demonstrate the effectiveness of the proposed methods.

The growth in the use of machine learning in different areas has revealed challenging classification problems that require robust systems. Multiple Classier Systems (MCSs) have attracted interest from researchers as a method that could address such problems. Optimizing the fitness of an MCS improves its, robustness. The lack of an analysis for MCSs from a fitness perspective is identified. To fill this gap, an analytic model from this perspective is derived mathematically by extending the error analysis introduced by Brown and Kuncheva in 2010. The model relates the fitness of an MCS to the average accuracy, positive-diversity, and negative-diversity of the classifiers that constitute the MCS. The model is verified using a statistical analysis of a Monte-Carlo based simulation. This shows the significance of the indicated relationships by the model. This model provides guidelines for developing robust MCSs. It enables the selection of classifiers which compose an MCS with an improved fitness while improving computational cost by avoiding local calculations. The usefulness of the model for designing classification systems is investigated. A new measure consisting of the accuracy and positive-diversity is developed. This measure evaluates fitness while avoiding many calculations compared to the regular measures. A new system (Gadapt) is developed. Gadapt combines machine learning and genetic algorithms to define subsets of the feature space that closely match true class regions. It uses the new measure as a multi-objective criterion for a multi-objective genetic algorithm to identify the MCSs those create the subsets. The design of Gadapt is validated experimentally. The usefulness of the measure and the method of determining the subsets for the performance of Gadapt are examined based on five generated data sets that represent a wide range of problems. The robustness of Gadapt to small amounts of training data is evaluated in comparison with five existing systems on four benchmark data sets. The performance of Gadapt is evaluated in comparison with eleven existing systems on nine benchmark data sets. The analysis of the experiment results supports the validity of the Gadapt design and the outperforming of Gadapt on the existing systems in terms of robustness and performance.

Šioje disertacijoje nagrinėjamas sveikatos priežiūros įstaigos darbuotojų darbų grafikų optimizavimo uždavinys, kuris formuluojamas ir sprendžiamas, remiantis vienos didžiausių Lietuvos sveikatos priežiūros įstaigų, realiais duomenimis. Disertacijoje apžvelgiami darbų grafikų optimizavimo uždaviniai bei jų sprendimo metodai, atlikta naujausių šaltinių, tiriančių panašius uždavinius, analizė. Antrame skyriuje nagrinėjamasis darbų grafikų optimizavimo uždavinys suformuluotas matematiškai. Pateikiamos dvi formuluotės: vienakriterio ir daugiakriterio optimizavimo uždavinio. Aprašomos sąlygos, kurias turi tenkinti sudaromasis darbų grafikas. Trečiajame skyriuje nagrinėjami metodai, tiek vienakriteriams, tiek daugiakriteriams uždaviniams spręsti. Pasiūlytas naujas metodas, kuris efektyviau nei kiti nagrinėti metodai sprendžia šioje disertacijoje suformuluotą uždavinį. Ketvirtame skyriuje pateikiami pasiūlyto metodo eksperimentinio tyrimo rezultatai. Pirmoje skyriaus dalyje analizuojami rezultatai gauti, sprendžiant vienakriterį optimizavimo uždavinį, o antroje dalyje – daugiakriterį optimizavimo uždavinį. Disertacijos tyrimų rezultatai buvo pristatyti respublikinėje konferencijoje ir trijose tarptautinėse konferencijose bei publikuoti trijuose mokslo žurnaluose. / In this dissertation nurse rostering problem is investigated. The formulation of the problem is based on real-world data of one of the largest healthcare centers in Lithuania. Most recent publications that tackle the nurse rostering problem and the methods for solving the nurse rostering problem are reviewed in this dissertation. The mathematical formulation of the single objective and the multi-objective nurse rostering problem is presented and the requirements for the roster are described in the second chapter. In the third chapter, the methods for solving the single objective and the multi-objective nurse rostering problem are described. A new method for solving the single objective and the multi-objective nurse rostering problem is proposed in the third chapter. In the fourth chapter, the experimental results of our proposed method are introduced. In the first section of this chapter, the results gathered solving single-objective optimization problem are analyzed, and in the second section of this chapter, the results gathered solving multi-objective optimization problem are analyzed. Dissertation research results were presented at one national conference and three international conferences and published in three scientific journals.

Global optimization problems are relevant in various fields of research and industry, such as chemistry, biology, biomedicine, operational research, etc. Normally it is easier to solve optimization problems having some specific properties of objective function such as linearity, convexity, differentiability, etc. However, there are a lot of practical problems that do not satisfy such properties or even cannot be expressed in an adequate mathematical form. Therefore, it is popular to use random search optimization methods in solving such optimization problems. The dissertation deals with investigation of random search global optimization algorithms, their parallelization and application to solve practical problems. The work is focused on modification and parallelization of particle swarm optimization and genetic algorithms. The modification of particle swarm optimization algorithm, based on reduction of the search area is proposed, and several strategies to parallelize the algorithm are investigated. The algorithm is applied to solve Multiple Gravity Assist problem using parallel computing system. A hybrid global multi-objective optimization algorithm is developed by modifying single agent stochastic search strategy, and incorporating it into multi-objective optimization genetic algorithm. Several strategies to parallelize multi-objective optimization genetic algorithm is proposed. Parallel algorithms are experimentally investigated by solving competitive facility location... [to full text] / Optimizavimo uždaviniai sutinkami įvairiose mokslo ir pramonės srityse, tokiose kaip chemija, biologija, biomedicina, operacijų tyrimai ir pan. Paprastai efektyviausiai sprendžiami uždaviniai, turintys tam tikras savybes, tokias kaip tikslo funkcijų tiesiškumas, iškilumas, diferencijuojamumas ir pan. Tačiau ne visi praktikoje pasitaikantys optimizavimo uždaviniai tenkina šias savybes, o kartais iš vis negali būti išreiškiami adekvačia matematine išraiška. Tokiems uždaviniam spręsti yra populiarūs atsitiktinės paieškos optimizavimo metodai. Disertacijoje yra tiriami atsitiktinės paieškos optimizavimo metodai, jų lygiagretinimo galimybės ir taikymas praktikoje pasitaikantiems uždaviniams spręsti. Pagrindinis dėmesys skiriamas dalelių spiečiaus optimizavimo ir genetinių algoritmų modifikavimui ir lygiagretinimui. Disertacijoje yra siūloma dalelių spiečiaus optimizavimo algoritmo modifikacija, grįsta pieškos srities siaurinimu, ir tiriamos kelios algoritmo lygiagretinimo strategijos. Algoritmas yra taikomas erdvėlaivių skrydžių trajektorijų optimizavimo uždaviniui spręsti lygiagrečiųjų skaičiavimų sistemose. Taip pat yra siūlomas hibridinis globaliojo daugiakriterio optimizavimo algoritmas, gautas modifikuojant vieno agento stochastinės paieškos algoritmą ir įkomponuojant į daugiakriterio optimizavimo genetinį algoritmą. Siūlomos kelios daugiakriterio genetinio algoritmo lygiagretinimo strategijos. Jų pagrindu gauti lygiagretieji algoritmai eksperimentiškai tiriami sprendžiant... [toliau žr. visą tekstą]

Simulation-based optimization methodologies are widely applied in real world optimization problems. In developing these methodologies, beside simulation models, algorithms play a critical role. One example is an evolutionary multi objective optimization algorithm known as Reference point-based Non-dominated Sorting Genetic Algorithm-II (R-NSGA-II), which has shown to have some promising results in this regard. Its successor, R-NSGA-II-adaptive diversity control (hereafter Adaptive Epsilon-R-NSGA-II (AE-R-NSGA-II) algorithm) is one of the latest proposed extensions of the R-NSGA-II algorithm and in the early stages of its development. So far, little research exists on its applicability and usefulness, especially in real world optimization problems. This thesis evaluates behavior and performance of AE-R-NSGA-II, and to the best of our knowledge is one of its kind. To this aim, we have investigated the algorithm in two experiments, using two benchmark functions, 10 performance measures, and a behavioral characteristics analysis method. The experiments are designed to (i) assess behavior and performance of AE-R-NSGA-II, (ii) and facilitate efficient use of the algorithm in real world optimization problems. This is achieved through the algorithm parameter configuration (parametric study) according to the problem characteristics. The behavior and performance of the algorithm in terms of diversity of the solutions obtained, and their convergence to the optimal Pareto front is studied in the first experiment through manipulating a parameter of the algorithm referred to as Adaptive epsilon coefficient value (C), and in the second experiment through manipulating the Reference point (R) according to the distance between the reference point and the global Pareto front. Therefore, as one contribution of this study two new diversity performance measures (called Modified spread, and Population diversity), and the behavioral characteristics analysis method called R-NSGA-II adaptive epsilon value have been introduced and applied. They can be modified and applied for the evaluation of any reference point based algorithm such as the AE-R-NSGA-II. Additionally, this project contributed to improving the Benchmark software, for instance by identifying new features that can facilitate future research in this area. Some of the findings of the study are as follows: (i) systematic changes of C and R parameters influence the diversity and convergence of the obtained solutions (to the optimal Pareto front and to the reference point), (ii) there is a tradeoff between the diversity and convergence speed, according to the systematic changes in the settings, (iii) the proposed diversity measures and the method are applicable and useful in combination with other performance measures. Moreover, we realized that because of the unexpected abnormal behaviors of the algorithm, in some cases the results are conflicting, therefore, impossible to interpret. This shows that still further research is required to verify the applicability and usefulness of AE-R-NSGA-II in practice. The knowledge gained in this study helps improving the algorithm.

Optimizavimo uždaviniai sutinkami įvairiose mokslo ir pramonės srityse, tokiose kaip chemija, biologija, biomedicina, operacijų tyrimai ir pan. Paprastai efektyviausiai sprendžiami uždaviniai, turintys tam tikras savybes, tokias kaip tikslo funkcijų tiesiškumas, iškilumas, diferencijuojamumas ir pan. Tačiau ne visi praktikoje pasitaikantys optimizavimo uždaviniai tenkina šias savybes, o kartais iš vis negali būti išreiškiami adekvačia matematine išraiška. Tokiems uždaviniam spręsti yra populiarūs atsitiktinės paieškos optimizavimo metodai. Disertacijoje yra tiriami atsitiktinės paieškos optimizavimo metodai, jų lygiagretinimo galimybės ir taikymas praktikoje pasitaikantiems uždaviniams spręsti. Pagrindinis dėmesys skiriamas dalelių spiečiaus optimizavimo ir genetinių algoritmų modifikavimui ir lygiagretinimui. Disertacijoje yra siūloma dalelių spiečiaus optimizavimo algoritmo modifikacija, grįsta pieškos srities siaurinimu, ir tiriamos kelios algoritmo lygiagretinimo strategijos. Algoritmas yra taikomas erdvėlaivių skrydžių trajektorijų optimizavimo uždaviniui spręsti lygiagrečiųjų skaičiavimų sistemose. Taip pat yra siūlomas hibridinis globaliojo daugiakriterio optimizavimo algoritmas, gautas modifikuojant vieno agento stochastinės paieškos algoritmą ir įkomponuojant į daugiakriterio optimizavimo genetinį algoritmą. Siūlomos kelios daugiakriterio genetinio algoritmo lygiagretinimo strategijos. Jų pagrindu gauti lygiagretieji algoritmai eksperimentiškai tiriami sprendžiant... [toliau žr. visą tekstą] / Global optimization problems are relevant in various fields of research and industry, such as chemistry, biology, biomedicine, operational research, etc. Normally it is easier to solve optimization problems having some specific properties of objective function such as linearity, convexity, differentiability, etc. However, there are a lot of practical problems that do not satisfy such properties or even cannot be expressed in an adequate mathematical form. Therefore, it is popular to use random search optimization methods in solving such optimization problems. The dissertation deals with investigation of random search global optimization algorithms, their parallelization and application to solve practical problems. The work is focused on modification and parallelization of particle swarm optimization and genetic algorithms. The modification of particle swarm optimization algorithm, based on reduction of the search area is proposed, and several strategies to parallelize the algorithm are investigated. The algorithm is applied to solve Multiple Gravity Assist problem using parallel computing system. A hybrid global multi-objective optimization algorithm is developed by modifying single agent stochastic search strategy, and incorporating it into multi-objective optimization genetic algorithm. Several strategies to parallelize multi-objective optimization genetic algorithm is proposed. Parallel algorithms are experimentally investigated by solving competitive facility location... [to full text]

In this thesis, optimal placement of adhesively bonded piezoelectric patches on laminated plates and the determination of geometry of the bonding area to maximize actuation effect are studied. A new finite element model, in which each layer is considered to be a separate plate, is developed. The adhesive layer is modeled as a distributed spring system. In this way, relative transverse normal and shear motion of the layers are allowed. Effect of delamination on the adhesive layer stresses is also studied and investigated through several case studies. Optimization problems, having single and multiple objectives, are investigated for both actuator placement and selective bonding examples. In these case studies, 2D and 3D Pareto fronts are also obtained. &amp / #8216 / Hide and Seek Simulated Annealing&amp / #8217 / method is adapted for discrete problems and used as the optimization technique for single-objective problems. Finally, Multiple Cooling Multi Objective Simulated Annealing optimization algorithm is adapted and used in multi-objective optimization case studies.

Multiple Cooling Multi Objective Simulated Annealing algorithm has been combined with a conceptual design code written by the author to carry out a multi objective design optimization of an Agricultural Aerial Robot. Both the single and the multi objective optimization problems are solved. The performance figures of merits for different aircraft configurations are compared. In this thesis the potential of optimization as a powerful design tool to the aerospace problems is demonstrated.