Generalized Differential Evolution for Global Multi-Objective Optimization with Constraints

The objective of this thesis work is to develop and study the Differential Evolution Algorithm for multi-objective optimization with constraints. Differential Evolution is an evolutionary algorithm that has gained in popularity because of its simplicity and good observed performance. Multi-objective evolutionary algorithms have become popular since they are able to produce a set of compromise solutions during the search process to approximate the Pareto-optimal front. The starting point for this thesis was an idea how Differential Evolution, with simple changes, could be extended for optimization with multiple constraints and objectives. This approach is implemented, experimentally studied, and further developed in the work. Development and study concentrates on the multi-objective optimization aspect. The main outcomes of the work are versions of a method called Generalized Differential Evolution. The versions aim to improve the performance of the method in multi-objective optimization. A diversity preservation technique that is effective and efficient compared to previous diversity preservation techniques is developed. The thesis also studies the influence of control parameters of Differential Evolution in multi-objective optimization. Proposals for initial control parameter value selection are given. Overall, the work contributes to the diversity preservation of solutions in multi-objective optimization.

Ranking-Dominance Relation for (Evolutionary) Multi-Objective Optimization

An alternative relation to Pareto-dominance relation is proposed. The new relation is based on ranking a set of solutions according to each separate objective and an aggregation function to calculate a scalar fitness value for each solution. The relation is called as ranking-dominance and it tries to tackle the curse of dimensionality commonly observedin evolutionary multi-objective optimization. Ranking-dominance can beused to sort a set of solutions even for a large number of objectives when Pareto-dominance relation cannot distinguish solutions from one another anymore. This permits search to advance even with a large number of objectives. It is also shown that ranking-dominance does not violate Pareto-dominance. Results indicate that selection based on ranking-dominance is able to advance search towards the Pareto-front in some cases, where selection based on Pareto-dominance stagnates. However, in some cases it is also possible that search does not proceed into direction of Pareto-front because the ranking-dominance relation permits deterioration of individual objectives. Results also show that when the number of objectives increases, selection based on just Pareto-dominance without diversity maintenance is able to advance search better than with diversity maintenance. Therefore, diversity maintenance is connive at the curse of dimensionality.

Testing Platform Development for Large Scale Solar Integration

Currently, a high penetration level of Distributed Generations (DGs) has been observed in the Danish distribution systems, and even more DGs are foreseen to be present in the upcoming years. How to utilize them for maintaining the security of the power supply under the emergency situations, has been of great interest for study. This master project is intended to develop a control architecture for studying purposes of distribution systems with large scale integration of solar power. As part of the EcoGrid EU Smart Grid project, it focuses on the system modelling and simulation of a Danish representative LV network located in Bornholm island. Regarding the control architecture, two types of reactive control techniques are implemented and compare. In addition, a network voltage control based on a tap changer transformer is tested. The optimized results after applying a genetic algorithm to five typical Danish domestic loads are lower power losses and voltage deviation using Q(U) control, specially with large consumptions. Finally, a communication and information exchange system is developed with the objective of regulating the reactive power and thereby, the network voltage remotely and real-time. Validation test of the simulated parameters are performed as well.

Conflict-based Method for Conceptual Process Synthesis

The changing business environment demands that chemical industrial processes be designed such that they enable the attainment of multi-objective requirements and the enhancement of innovativedesign activities. The requirements and key issues for conceptual process synthesis have changed and are no longer those of conventional process design; there is an increased emphasis on innovative research to develop new concepts, novel techniques and processes. A central issue, how to enhance the creativity of the design process, requires further research into methodologies. The thesis presentsa conflict-based methodology for conceptual process synthesis. The motivation of the work is to support decision-making in design and synthesis and to enhance the creativity of design activities. It deals with the multi-objective requirements and combinatorially complex nature of process synthesis. The work is carriedout based on a new concept and design paradigm adapted from Theory of InventiveProblem Solving methodology (TRIZ). TRIZ is claimed to be a `systematic creativity' framework thanks to its knowledge based and evolutionary-directed nature. The conflict concept, when applied to process synthesis, throws new lights on design problems and activities. The conflict model is proposed as a way of describing design problems and handling design information. The design tasks are represented as groups of conflicts and conflict table is built as the design tool. The general design paradigm is formulated to handle conflicts in both the early and detailed design stages. The methodology developed reflects the conflict nature of process design and synthesis. The method is implemented and verified through case studies of distillation system design, reactor/separator network design and waste minimization. Handling the various levels of conflicts evolve possible design alternatives in a systematic procedure which consists of establishing an efficient and compact solution space for the detailed design stage. The approach also provides the information to bridge the gap between the application of qualitative knowledge in the early stage and quantitative techniques in the detailed design stage. Enhancement of creativity is realized through the better understanding of the design problems gained from the conflict concept and in the improvement in engineering design practice via the systematic nature of the approach.

Evolutionary computing in search-based software engineering

This master’s thesis aims to study and represent from literature how evolutionary algorithms are used to solve different search and optimisation problems in the area of software engineering. Evolutionary algorithms are methods, which imitate the natural evolution process. An artificial evolution process evaluates fitness of each individual, which are solution candidates. The next population of candidate solutions is formed by using the good properties of the current population by applying different mutation and crossover operations. Different kinds of evolutionary algorithm applications related to software engineering were searched in the literature. Applications were classified and represented. Also the necessary basics about evolutionary algorithms were presented. It was concluded, that majority of evolutionary algorithm applications related to software engineering were about software design or testing. For example, there were applications about classifying software production data, project scheduling, static task scheduling related to parallel computing, allocating modules to subsystems, N-version programming, test data generation and generating an integration test order. Many applications were experimental testing rather than ready for real production use. There were also some Computer Aided Software Engineering tools based on evolutionary algorithms.

Prediction in transformed images

The study is related to lossless compression of greyscale images. The goal of the study was to combine two techniques of lossless image compression, i.e. Integer Wavelet Transform and Differential Pulse Code Modulation to attain better compression ratio. This is an experimental study, where we implemented Integer Wavelet Transform, Differential Pulse Code Modulation and an optimized predictor model using Genetic Algorithm. This study gives encouraging results for greyscale images. We achieved a better compression ration in term of entropy for experiments involving quadrant of transformed image and using optimized predictor coefficients from Genetic Algorithm. In an other set of experiments involving whole image, results are encouraging and opens up many areas for further research work like implementing Integer Wavelet Transform on multiple levels and finding optimized predictor at local levels.

Statistical Optimum Design of Heat Exchangers

The optimal design of a heat exchanger system is based on given model parameters together with given standard ranges for machine design variables. The goals set for minimizing the Life Cycle Cost (LCC) function which represents the price of the saved energy, for maximizing the momentary heat recovery output with given constraints satisfied and taking into account the uncertainty in the models were successfully done. Nondominated Sorting Genetic Algorithm II (NSGA-II) for the design optimization of a system is presented and implemented inMatlab environment. Markov ChainMonte Carlo (MCMC) methods are also used to take into account the uncertainty in themodels. Results show that the price of saved energy can be optimized. A wet heat exchanger is found to be more efficient and beneficial than a dry heat exchanger even though its construction is expensive (160 EUR/m2) compared to the construction of a dry heat exchanger (50 EUR/m2). It has been found that the longer lifetime weights higher CAPEX and lower OPEX and vice versa, and the effect of the uncertainty in the models has been identified in a simplified case of minimizing the area of a dry heat exchanger.

Design and Comparison оf Mixed Hinf/H2 Controller fоr АMB System

This master’s thesis is focused on the active magnetic bearings control, specifically the robust control. As carrying out of such kind of control used mixed H2/Hinf controller. So the goal of this work is to design it using Robust Control Toolbox™ in MATLAB and compare it performance and robustness with Hinf robust controller characteristics. But only one degree-of-freedom controller considered.

Lautanauhaohjeen muodostus tavoitekuviosta geneettisellä algoritmilla

Lautanauhatekniikka on monipuolinen menetelmä esimerkiksi kuvioitujen nauhojen kutomiseen, mutta uusien kuvioaiheiden suunnittelu, tai aloittelijalle jo valmiiden ohjeettomien kuviomallien jäljittely, voi helposti käydä työlääksi menetelmän ominaispiirteiden johdosta. Tämän työn tavoitteena oli kehittää ohjelmallinen työkalu auttamaan näissä ongelmissa automatisoimalla kudontaohjeen etsintä käyttäjän laatimalle tavoitekuviolle. Ratkaisumenetelmän perustaksi valittiin geneettinen algoritmi, minkä johdosta työn keskeisintutkimusongelma oli kartoittaa algoritmin perusoperaatioiden parametrien ja tavoitekuvion kompleksisuuden keskinäisiä riippuvuuksia riittävästi toimivien arvosuositusten antamiseen ohjelman tulevassa käytännön käytössä. Työssä ei kehitetty sovellusalueeseen mukautettuja evoluutiooperaatioita, vaan keskityttiin luomaan hyvin tunnetuista elementeistä perusta, jota voi myöhemmin kehittää eteenpäin.

Modeling of burden distribution in the blast furnace

The blast furnace is the main ironmaking production unit in the world which converts iron ore with coke and hot blast into liquid iron, hot metal, which is used for steelmaking. The furnace acts as a counter-current reactor charged with layers of raw material of very different gas permeability. The arrangement of these layers, or burden distribution, is the most important factor influencing the gas flow conditions inside the furnace, which dictate the efficiency of the heat transfer and reduction processes. For proper control the furnace operators should know the overall conditions in the furnace and be able to predict how control actions affect the state of the furnace. However, due to high temperatures and pressure, hostile atmosphere and mechanical wear it is very difficult to measure internal variables. Instead, the operators have to rely extensively on measurements obtained at the boundaries of the furnace and make their decisions on the basis of heuristic rules and results from mathematical models. It is particularly difficult to understand the distribution of the burden materials because of the complex behavior of the particulate materials during charging. The aim of this doctoral thesis is to clarify some aspects of burden distribution and to develop tools that can aid the decision-making process in the control of the burden and gas distribution in the blast furnace. A relatively simple mathematical model was created for simulation of the distribution of the burden material with a bell-less top charging system. The model developed is fast and it can therefore be used by the operators to gain understanding of the formation of layers for different charging programs. The results were verified by findings from charging experiments using a small-scale charging rig at the laboratory. A basic gas flow model was developed which utilized the results of the burden distribution model to estimate the gas permeability of the upper part of the blast furnace. This combined formulation for gas and burden distribution made it possible to implement a search for the best combination of charging parameters to achieve a target gas temperature distribution. As this mathematical task is discontinuous and non-differentiable, a genetic algorithm was applied to solve the optimization problem. It was demonstrated that the method was able to evolve optimal charging programs that fulfilled the target conditions. Even though the burden distribution model provides information about the layer structure, it neglects some effects which influence the results, such as mixed layer formation and coke collapse. A more accurate numerical method for studying particle mechanics, the Discrete Element Method (DEM), was used to study some aspects of the charging process more closely. Model charging programs were simulated using DEM and compared with the results from small-scale experiments. The mixed layer was defined and the voidage of mixed layers was estimated. The mixed layer was found to have about 12% less voidage than layers of the individual burden components. Finally, a model for predicting the extent of coke collapse when heavier pellets are charged over a layer of lighter coke particles was formulated based on slope stability theory, and was used to update the coke layer distribution after charging in the mathematical model. In designing this revision, results from DEM simulations and charging experiments for some charging programs were used. The findings from the coke collapse analysis can be used to design charging programs with more stable coke layers.