Document clustering with optimized unsupervised feature selection and centroid allocation

An effective document clustering system can significantly improve the tasks of document analysis, grouping, and retrieval. The performance of a document clustering system mainly depends on document preparation and allocation of cluster positions. As achieving optimal document clustering is a combinatorial NP-hard optimization problem, it becomes essential to utilize non-traditional methods to look for optimal or near-optimal solutions. During the allocation of cluster positions or the centroids allocation process, the extra text features that represent keywords in each document have an effect on the clustering results. A large number of features need to be reduced using dimensionality reduction techniques. Feature selection is an important step that can be used to reduce the redundant and inconsistent features. Due to a large number of the potential feature combinations, text feature selection is considered a complicated process. The persistent drawbacks of the current text feature selection methods such as local optima and absence of class labels of features were addressed in this thesis. The supervised and unsupervised feature selection methods were investigated. To address the problems of optimizing the supervised feature selection methods so as to improve document clustering, memetic hybridization between filter and wrapper feature selection, known as Memetic Algorithm Feature Selection, was presented first. In order to deal with the unlabelled features, unsupervised feature selection method was also proposed. The proposed unsupervised feature selection method integrates Simulated Annealing to the global search using Differential Evolution. This combination also aims to combine the advantages of both the wrapper and filter methods in a memetic scheme but on an unsupervised basis. Two versions of this hybridization were proposed. The first was named Differential Evolution Simulated Annealing, which uses the standard mutation of Differential Evolution, and the second was named Dichotomous Differential Evolution Simulated Annealing, which used the dichotomous mutation of the differential evolution. After feature selection two centroid allocation methods were proposed; the first is the combination of Chaotic Logistic Search and Discrete Differential Evolution global search, which was named Differential Evolution Memetic Clustering (DEMC) and the second was based on using the Gradient search using the k-means as a local search with a modified Differential Harmony global Search. The resulting method was named Memetic Differential Harmony Search (MDHS). In order to intensify the exploitation aspect of MDHS, a binomial crossover was used with it. Finally, the improved method is named Crossover Memetic Differential Harmony Search (CMDHS). The test results using the F-measure, Average Distance of Document to Cluster (ADDC) and the nonparametric statistical tests showed the superiority of the CMDHS over the baseline methods, namely the HS, DHS, k-means and the MDHS. The tests also show that CMDHS is better than the DEMC proposed earlier. Finally the proposed CMDHS was compared with two current state-of-the-art methods, namely a Krill Herd (KH) based centroid allocation method and an Artifice Bee Colony (ABC) based method, and found to outperform these two methods in most cases

Game theoretic modeling and analysis : A co-evolutionary, agent-based approach

Ph.DDOCTOR OF PHILOSOPH

Music in Evolution and Evolution in Music

Music in Evolution and Evolution in Music by Steven Jan is a comprehensive account of the relationships between evolutionary theory and music. Examining the ‘evolutionary algorithm’ that drives biological and musical-cultural evolution, the book provides a distinctive commentary on how musicality and music can shed light on our understanding of Darwin’s famous theory, and vice-versa. Comprised of seven chapters, with several musical examples, figures and definitions of terms, this original and accessible book is a valuable resource for anyone interested in the relationships between music and evolutionary thought. Jan guides the reader through key evolutionary ideas and the development of human musicality, before exploring cultural evolution, evolutionary ideas in musical scholarship, animal vocalisations, music generated through technology, and the nature of consciousness as an evolutionary phenomenon. A unique examination of how evolutionary thought intersects with music, Music in Evolution and Evolution in Music is essential to our understanding of how and why music arose in our species and why it is such a significant presence in our lives

Scalable parallel evolutionary optimisation based on high performance computing

Evolutionary algorithms (EAs) have been successfully applied to solve various challenging optimisation problems. Due to their stochastic nature, EAs typically require considerable time to find desirable solutions; especially for increasingly complex and large-scale problems. As a result, many works studied implementing EAs on parallel computing facilities to accelerate the time-consuming processes. Recently, the rapid development of modern parallel computing facilities such as the high performance computing (HPC) bring not only unprecedented computational capabilities but also challenges on designing parallel algorithms. This thesis mainly focuses on designing scalable parallel evolutionary optimisation (SPEO) frameworks which run efficiently on the HPC. Motivated by the interesting phenomenon that many EAs begin to employ increasingly large population sizes, this thesis firstly studies the effect of a large population size through comprehensive experiments. Numerical results indicate that a large population benefits to the solving of complex problems but requires a large number of maximal fitness evaluations (FEs). However, since sequential EAs usually requires a considerable computing time to achieve extensive FEs, we propose a scalable parallel evolutionary optimisation framework that can efficiently deploy parallel EAs over many CPU cores at CPU-only HPC. On the other hand, since EAs using a large number of FEs can produce massive useful information in the course of evolution, we design a surrogate-based approach to learn from this historical information and to better solve complex problems. Then this approach is implemented in parallel based on the proposed scalable parallel framework to achieve remarkable speedups. Since demanding a great computing power on CPU-only HPC is usually very expensive, we design a framework based on GPU-enabled HPC to improve the cost-effectiveness of parallel EAs. The proposed framework can efficiently accelerate parallel EAs using many GPUs and can achieve superior cost-effectiveness. However, since it is very challenging to correctly implement parallel EAs on the GPU, we propose a set of guidelines to verify the correctness of GPU-based EAs. In order to examine these guidelines, they are employed to verify a GPU-based brain storm optimisation that is also proposed in this thesis. In conclusion, the comprehensively experimental study is firstly conducted to investigate the impacts of a large population. After that, a SPEO framework based on CPU-only HPC is proposed and is employed to accelerate a time-consuming implementation of EA. Finally, the correctness verification of implementing EAs based on a single GPU is discussed and the SPEO framework is then extended to be deployed based on GPU-enabled HPC

Passive localization model in wireless sensor networks based on adaptive hybrid heuristic algorithms

Предмет истраживања ове докторске дисертације је проблем пасивног лоцирања заснован на мерењу времена пропагације сигнала (Time of Arrival, ТОА), или временске разлике пропагације сигнала (Time Difference of Arrival, TDOA) ради одређивања непознате локације неког објекта. За постављене моделе лоцирања формирана је функција максималне веродостојности (Maximum Likelihood, ML) са Гаусовом случајном расподелом за грешку мерења. Разматрани естимациони модел описан је нелинеарном, неконвексном функцијом циља, односно мултимодалном функцијом. При томе, за формирану функцију циља, глобално оптимално решење не може се нумерички одредити класичним методама оптимизације...The research in this dissertation is focused on the problem of passive target localization based on the noisy time of arrival (TOA) or time Difference of Arrival (TDOA) measurements, with the aim to accurately estimate the unknown passive target location. The maximum likelihood (ML) estimation problem is formulated for the considered localization problem, with measurement errors modelled as Gaussian distributed random variables. However, the ML objective function of the considered estimation problem is nonlinear and multimodal function, and in this case, the global optimal solution cannot be determined numerically by classical optimization methods..

Training issues and learning algorithms for feedforward and recurrent neural networks

Ph.DDOCTOR OF PHILOSOPH

Optimisation of both energy use and pumping costs in water distribution networks with several water sources using the setpoint curve

La optimización de los sistemas de bombeo se suele realizar a través de las curvas características de la red (asociadas con las curvas resistentes). Estas curvas están sujetas a la resistencia generada por el usuario en función de las necesidades de caudal y presión en cada punto de consumo. Dicha resistencia es muy variable y difícil de determinar lo que hace que el cálculo de las curvas sea poco práctico. El problema radica en que al no definirse adecuadamente las curvas resistentes, no se conocen las necesidades reales de caudal y presión de la red. Por lo tanto no se puede estimar el exceso de energía aportado por las bombas ni el aumento de costos de operación que esto representa. Sin embargo, existe otro tipo de curva denominada curva de consigna (CC). Dicha curva ha sido poco estudiada hasta ahora y su cálculo es relativamente fácil. De ahí que el presente trabajo tiene como objetivo la optimización de la energía y de los costes de bombeo mediante el uso de la CC. Para cumplir con el objetivo, primero se estudia el cálculo de la CC en redes de agua con múltiples estaciones de bombeo, consumos no dependientes y dependientes de la presión, y sin tanques de almacenamiento. Posteriormente, se lleva a cabo la optimización sólo desde la perspectiva energética (caudal y altura de bombeo). Para ello se realiza la búsqueda de la distribución de caudales óptima entre las estaciones de bombeo que conlleve a las CC óptimas. Se proponen dos métodos: uno discreto (M-D) y otro continuo (M-C). El M-D considera la distribución de caudales como una variable discreta. La distribución óptima se obtiene de un conjunto de soluciones previamente definidas. En el M-C la distribución de caudales se asume como variable continua. La solución óptima viene dada por el uso de un algoritmo de optimización. Se han usado los algoritmos: Hooke-Jeeves y Nelder-Mead. El siguiente paso es la optimización de costos de operación (costos de bombeo y costos de producción de agua). Para ello se parte del M-C y se incluyen además las tarifas de energía, las tarifas de producción de agua y un criterio sobre el rendimiento mínimo esperado en las estaciones de bombeo. El último paso consiste en la optimización energética y de costos en redes con tanques. La inclusión de tanques implica la modificación del cálculo de la CC y por ende la metodología de optimización. En la función de costes se incluyen además de los costes de bombeo y de producción, costes de penalización por incumplimiento de presiones y de volúmenes de reserva. Al incluirse los tanques, incrementa el número de variables de decisión. Por lo que, es necesario el uso de algoritmos más potentes. Se han usado el Differential Evolution y el Hybrid Algorithm. Este último es un aporte añadido de este trabajo. La metodología de optimización se aplica a cinco redes de distribución: TF, Catinen, Coplaca, Anytown y Richmond. Los tanques sólo se consideran en las dos últimas redes. En el caso de la red TF se realiza una demostración sin entrar en profundidad de la selección de bombas a través del uso de las CC óptimas. Sin embargo, este paso está fuera de los límites de este trabajo. Tampoco se consideran condiciones de operación múltiples de la red, ni la fiabilidad en el caso de la remoción de tanques o sistemas de bombeo. No obstante, los resultados obtenidos evidencian que los sistemas de bombeo operados usando la CC pueden mejorar sus costos operativos hasta en un 12%. La metodología proporciona información sobre las estaciones de bombeo que representan mayores ahorros frente a aquellas que son menos importantes o innecesarias. Además, el método ha permitido demostrar que mejores condiciones de bombeo (bajas tarifas de energía y altos rendimientos) no siempre significan menores costos de operación. Finalmente, algunos resultados muestran la posible utilidad del método para optimizar tanto el uso como la ubicación de los tanquUsually, pumping optimisation in water distribution networks is carried out by means of the system head curves (SHCs), also known as the resistance curves (RCs). These curves are subjected to the resistance generated by the users, according with the flow and pressure head needs at the final points of demand. Such resistance is highly variable and hard to determine. Thus the calculation of the RCs and all the points that define them results impractical. As a RCs suitable calculation is not possible, real flow and pressure needs of the network are not known. Therefore, neither energy excess of the pumping regarding the real requirements nor the raising operating costs due to such excess, are estimated. However, there is another type of SHC defined as the setpoint curve (SC). It can be easily calculated, but has been poorly studied so far. Thus, this work aims the optimisation of the energy use and operating costs in pumping systems by using the SCs. To achieve the objective, first of all the SC calculation is studied for networks with several pumping stations, non-pressure driven demands, pressure-driven demands, and without tanks. Next, the optimisation is performed only from the energy point of view (i.e. flow and pumping head required). For that, a search of the optimum flow distribution among pumping stations to find the optimum SCs is performed. Two methods are proposed: the discrete (D-M) and the continuous (C-M). The D-M considers the flow distribution as a discrete variable. The optimum flow distribution is obtained from a set of solutions defined previously. In the C-M, the flow distribution is assumed as a continuous variable. The optimum solution comes from using optimisation algorithms. Two algorithms have been applied: Hooke-Jeeves and Nelder-Mead. Then, the cost optimisation (pumping cost and water production cost) is developed. For that purpose, the M-C is used as starting point. Then, energy tariffs, water production fares and the minimum expected efficiency at the pumping stations, are included. The last step consists in the energy and cost optimisation in networks with tanks. When tanks are included the SC calculation methodology changes. Hence, the optimisation process also does. In that sense, besides the costs of pumping and water production, the cost function also considers penalty costs for unaccomplished minimum pressures and minimum storage leves. Moreover, tanks inclusion also rises the number of decision variables. Thus, the use of more powerful algorithms is required. In that context, the Differential Evolution and the Hybrid Algorithm have been applied. The last one is an additional contribution of this work. The optimisation methodology is applied to five distribution networks: TF, Catinen, Coplaca, Anytown and Richmond. Tanks are only considered in the last two networks. In the case of TF network, demonstrative pumps selection (without going into great depth) by the optimum SCs application is done. However pumps sizing and selection study is out of the scope of this research. Neither multiple operation conditions nor reliability (i.e. in the case that tanks or pumping stations are removed), are considered. Nevertheless, the results obtained evidence that pumping systems operated by mean of the optimum SCs could reduce their operating costs up 12%. The methodology also gives information about which pumping stations represent major savings and which are less important or not needed. Besides, the method demonstrates that better pumping conditions (i.e. low energy tariffs and high efficiencies) not always mean lower operating costs. Finally, some results show that the method could be useful for the optimisation of both placement and use of storage tanks.L'optimització dels sistemes de bombament se sol realitzar a través de les corbes característiques de la xarxa (associades amb les corbes resistents). Aquestes corbes estan subjectes a la resistència generada per l'usuari en funció de les necessitats de cabal i pressió en cada punt de consum. Aquesta resistència és molt variable i difícil de determinar el que fa que el càlcul de les corbes siga poc pràctic. El problema radica que al no definir-se adequadament les corbes resistents, no es coneixen les necessitats reals de cabal i pressió de la xarxa. Per tant no es pot estimar l'excés d'energia aportat per les bombes ni l'augment de costos d'operació que açò representa. No obstant açò, existeix un altre tipus de corba denominada corba de consigna (CC). Aquesta corba ha sigut poc estudiada fins ara i el seu càlcul és relativament fàcil. Per aquest motiu el present treball té com a objectiu l'optimització de l'energia i dels costos de bombament mitjançant l'ús de la CC. Per a complir amb l'objectiu, primer s'estudia el càlcul de la CC en xarxes d'aigua amb múltiples estacions de bombament, consums no depenents i dependents de la pressió, i sense tancs d'emmagatzematge. Posteriorment, es duu a terme l'optimització només des de la perspectiva energètica (cabal i altura de bombament). Per a açò es realitza la cerca de la distribució de cabals òptima entre les estacions de bombament que comporte a les CC òptimes. Es proposen dos mètodes: un de discret (M-D) i un altre continu (M-C). El M-D considera la distribució de cabals com una variable discreta. La distribució òptima s'obté d'un conjunt de solucions prèviament definides. En el M-C la distribució de cabals s'assumeix com a variable contínua. La solució òptima ve donada per l'ús d'un algorisme d'optimització. S'han usat els algorismes: Hooke-Jeeves i Nelder-Mead. El següent pas és l'optimització de costos d'operació (costos de bombament i costos de producció d'aigua). Per a açò es parteix del M-C i s'inclouen a més les tarifes d'energia, les tarifes de producció d'aigua i un criteri sobre el rendiment mínim esperat en les estacions de bombament. L'últim pas consisteix en l'optimització energètica i de costos en xarxes amb tancs. La inclusió de tancs implica la modificació del càlcul de la CC i per tant la metodologia d'optimització. En la funció de costos s'inclouen a més dels costos de bombament i de producció, costos de penalització per incompliment de pressions i de volums de reserva. En incloure's els tancs, incrementa el nombre de variables de decisió. Pel que, és necessari l'ús d'algorismes més potents. S'han usat el Differential Evolution i el Hybrid Algorithm. Aquest últim és una aportació afegida d'aquest treball. La metodologia d'optimització s'aplica a cinc xarxes de distribució: TF, Catinen, Coplaca, Anytown i Richmond. Els tancs només es consideren en les dues últimes xarxes. En el cas de la xarxa TF es realitza una demostració sense entrar en profunditat de la selecció de bombes a través de l'ús de les CC òptimes. No obstant açò, aquest pas està fora dels límits d'aquest treball. Tampoc es consideren condicions d'operació múltiples de la xarxa, ni la fiabilitat en el cas de la remoció de tancs o sistemes de bombament. No obstant açò, els resultats obtinguts evidencien que els sistemes de bombament operats usant la CC poden millorar els seus costos operatius fins a en un 12%. La metodologia proporciona informació sobre les estacions de bombament que representen majors estalvis enfront d'aquelles que són menys importants o innecessàries. A més, el mètode ha permès demostrar que millors condicions de bombament (baixes tarifes d'energia i alts rendiments) no sempre signifiquen menors costos d'operació. Finalment, alguns resultats mostren la possible utilitat del mètode per a optimitzar tant l'ús com la ubicació dels tancs d'emmagatzematge.León Celi, CF. (2018). Optimisation of both energy use and pumping costs in water distribution networks with several water sources using the setpoint curve [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/107956TESI

Evolutionary algorithms for scheduling operations

While business process automation is proliferating through industries and processes, operations such as job and crew scheduling are still performed manually in the majority of workplaces. The linear programming techniques are not capable of automated production of a job or crew schedule within a reasonable computation time due to the massive sizes of real-life scheduling problems. For this reason, AI solutions are becoming increasingly popular, specifically Evolutionary Algorithms (EAs). However, there are three key limitations of previous studies researching application of EAs for the solution of the scheduling problems. First of all, there is no justification for the selection of a particular genetic operator and conclusion about their effectiveness. Secondly, the practical efficiency of such algorithms is unknown due to the lack of comparison with manually produced schedules. Finally, the implications of real-life implementation of the algorithm are rarely considered. This research aims at addressing all three limitations. Collaborations with DBSchenker,the rail freight carrier, and Garnett-Dickinson, the printing company,have been established. Multi-disciplinary research methods including document analysis, focus group evaluations, and interviews with managers from different levels have been carried out. A standard EA has been enhanced with developed within research intelligent operators to efficiently solve the problems. Assessment of the developed algorithm in the context of real life crew scheduling problem showed that the automated schedule outperformed the manual one by 3.7% in terms of its operating efficiency. In addition, the automatically produced schedule required less staff to complete all the jobs and might provide an additional revenue opportunity of £500 000. The research has also revealed a positive attitude expressed by the operational and IT managers towards the developed system. Investment analysis demonstrated a 41% return rate on investment in the automated scheduling system, while the strategic analysis suggests that this system can enable attainment of strategic priorities. The end users of the system, on the other hand, expressed some degree of scepticism and would prefer manual methods