A Multi-objective Evolutionary Algorithm to solve Complex Optimization Problems

Multi-objective optimization problem formulations reflect pragmatic modeling of several real-life complex optimization problems. In many of them the considered objectives are competitive with each other; emphasizing only one of them during solution generation and evolution incurs high probability of producing a one-sided solution, which is unacceptable with respect to other objectives. An appropriate solution to the multi-objective optimization problem is to investigate a set of solutions that satisfy all of the competing objectives to an acceptable extent, where no solution in the solution set is dominated by others in terms of objective optimization. In this work, we investigate well known Non-dominated Sorting Genetic Algorithm (NSGA-II), and Strength Pareto Evolutionary Algorithm (SPEA-II), to find Pareto optimal solutions for two real-life problems: Task-based Sailor Assignment Problem (TSAP) and Coverage and Lifetime Optimization Problem in Wireless Sensor Networks (CLOP). Both of these problems are multi-objective problems. TSAP constitutes five multi-directional objectives, whereas CLOP is composed of two competing objectives. To validate the special operators developed, these two test bed problems have been used. Finally, traditional NSGA-II and SPEA-II have been blended with these special operators to generate refined solutions of these multi-objective optimization problems

A Comparison of multiobjective evolutionary algorithms with informed initialization and kuhn-munkres algorithm for the sailor assignment problem

This paper examines the performance of two multiobjective evolutionary algorithms, NSGA-II and SPEA2, with informed initialization on large instances of United States Navy\u27s Sailor Assignment Problem. The informed initialization includes in the initial population special solutions obtained by an extension of the Kuhn-Munkres algorithm. The Kuhn-Munkres algorithm, a classical algorithm that solves in O(n3) time instances of the single valued linear assignment problem, is extended here to render it applicable on single objective instances of the sailor assignment problem obtained using weight vectors to scalarize the natural multiobjective formulation. The Kuhn-Munkres extension is also used to provide a performance benchmark for comparison with the evolutionary algorithms

Word order variation and string similarity algorithm to reduce pattern scripting in pattern matching conversational agents

This paper presents a novel sentence similarity algorithm designed to tackle the issue of free word order in the Urdu language. Free word order in a language poses many challenges when implemented in a conversational agent, primarily due to the fact that it increases the amount of scripting time needed to script the domain knowledge. A language with free word order like Urdu means a single phrase/utterance can be expressed in many different ways using the same words and still be grammatically correct. This led to the research of a novel string similarity algorithm which was utilized in the development of an Urdu conversational agent. The algorithm was tested through a black box testing methodology which involved processing different variations of scripted patterns through the system to gauge the performance and accuracy of the algorithm with regards to recognizing word order variations of the related scripted patterns. Initial testing has highlighted that the algorithm is able to recognize legal word order variations and reduce the knowledge base scripting of conversational agents significantly. Thus saving great time and effort when scripting the knowledge base of a conversational agent

Interference cancellation and Resource Allocation approaches for Device-to-Device Communications

Network assisted Device-to-Device (D2D) communication as an underlay to cellular spectrum has attracted much attention in mobile network standards for local area connectivity as a means to improve the cellular spectrum utilization and to reduce the energy consumption of User Equipments (UEs). The D2D communication uses resources of the underlying mobile network which results in different interference scenarios. These include interference from cellular to D2D link, D2D to cellular link and interference among D2D links when multiple D2D links share common resources. In this thesis, an orthogonal precoding interference cancellation method is initially presented to reduce the cellular to D2D and D2D to cellular interferences when the cellular channel resources are being shared by a single D2D link. Three different scenarios have been considered when establishing a D2D communication along with a Base Station-to-UE communication. The proposed method is analytically evaluated in comparison with the conventional precoding matrix allocation method in terms of ergodic capacity. This method is then extended for a cluster based multi-link D2D scenario where interference between D2D pairs also exists in addition to the other two interference scenarios. In this work, cluster denotes a group of devices locally communicating through multi-link D2D communications sharing the same radio resources of the Cluster Head. Performance of the proposed method is evaluated and compared for different resource sharing modes. The analyses illustrate the importance of cluster head in each cluster to save the battery life of devices in that cluster. The outage probability is considered as a performance evaluation matrix for guaranteeing QoS constrain of communication links. Hence, the mathematical expressions for outage probability of the proposed method for single-link and multi-link D2D communications are presented and compared with an existing interference cancellation technique. To execute the cluster based interference cancellation approach, a three-step resource allocation scheme is then proposed. It first performs a mode selection procedure to choose the transmission mode of each UEs. Then a clustering scheme is developed to group the links that can share a common resource to improve the spectral efficiency. For the selection of suitable cellular UEs for each cluster whose resource can be shared, a cluster head selection algorithm is also developed. Maximal residual energy and minimal transmit power have been considered as parameters for the cluster head selection scheme. Finally, the expression for maximum number of links that the radio resource of shared UE can support is analytically derived. The performance of the proposed scheme is evaluated using a WINNER II A1 indoor office model. The performance of D2D communication practically gets limited due to large distance and/or poor channel conditions between the D2D transmitter and receiver. To overcome these issues, a relay-assisted D2D communication is introduced in this thesis where a device relaying is an additional transmission mode along with the existing cellular and D2D transmission modes. A transmission mode assignment algorithm based on the Hungarian algorithm is then proposed to improve the overall system throughput. The proposed algorithm tries to solve two problems: a suitable transmission mode selection for each scheduled transmissions and a device selection for relaying communication between user equipments in the relay transmission mode. Simulation results showed that our proposed algorithm improves the system performance in terms of the overall system throughput and D2D data rate in comparison with traditional D2D communication schemes

Methodology and algorithms for Urdu language processing in a conversational agent

This thesis presents the research and development of a novel text based goal-orientated conversational agent (CA) for the Urdu language called UMAIR (Urdu Machine for Artificially Intelligent Recourse). A CA is a computer program that emulates a human in order to facilitate a conversation with the user. The aim is investigate the Urdu language and its lexical and grammatical features in order to, design a novel engine to handle the language unique features of Urdu. The weakness in current Conversational Agent (CA) engines is that they are not suited to be implemented in other languages which have grammar rules and structure totally different to English. From a historical perspective CA’s including the design of scripting engines, scripting methodologies, resources and implementation procedures have been implemented for the most part in English and other Western languages (i.e. German and Spanish). The development of an Urdu conversational agent has required the research and development of new CA framework which incorporates methodologies and components in order overcome the language unique features of Urdu such as free word order, inconsistent use of space, diacritical marks and spelling. The new CA framework was utilised to implement UMAIR. UMAIR is a customer service agent for National Database and Registration Authority (NADRA) designed to answer user queries related to ID card and Passport applications. UMAIR is able to answer user queries related to the domain through discourse with the user by leading the conversation using questions and offering appropriate advice with the intention of leading the discourse to a pre-determined goal. The research and development of UMAIR led to the creation of several novel CA components, namely a new rule based Urdu CA engine which combines pattern matching and sentence/string similarity techniques along with new algorithms to process user utterances. Furthermore, a CA evaluation framework has been researched and tested which addresses the gap in research to develop the evaluation of natural language systems in general. Empirical end user evaluation has validated the new algorithms and components implemented in UMAIR. The results show that UMAIR is effective as an Urdu CA, with the majority of conversations leading to the goal of the conversation. Moreover the results also revealed that the components of the framework work well to mitigate the challenges of free word order and inconsistent word segmentation

Unveiling Hidden Values of Optimization Models with Metaheuristic Approach

Considering that the decision making process for constrained optimization problem is based on modeling, there is always room for alternative solutions because there is usually a gap between the model and the real problem it depicts. This study looks into the problem of finding such alternative solutions, the non-optimal solutions of interest for constrained optimization models, the SoI problem. SoI problems subsume finding feasible solutions of interest (FoIs) and infeasible solutions of interest (IoIs). In all cases, the interest addressed is post-solution analysis in one form or another. Post-solution analysis of a constrained optimization model occurs after the model has been solved and a good or optimal solution for it has been found. At this point, sensitivity analysis and other questions of import for decision making come into play and for this purpose the SoIs can be very valuable. An evolutionary computation approach (in particular, a population-based metaheuristic) is proposed for solving the SoI problem and a systematic approach with a feasible-infeasible- two-population genetic algorithm is demonstrated. In this study, the effectiveness of the proposed approach on finding SoIs is demonstrated with generalized assignment problems and generalized quadratic assignment problems. Also, the applications of the proposed approach on the multi-objective optimization and robust-optimization issues are examined and illustrated with two-sided matching problems and flowshop scheduling problems respectively

Development of an Arabic conversational intelligent tutoring system for education of children with autism spectrum disorder

Children with Autism Spectrum Disorder (ASD) are affected in different degrees in terms of their level of intellectual ability. Some people with Asperger syndrome or high functioning autism are very intelligent academically but they still have difficulties in social and communication skills. In recent years, many of these pupils are taught within mainstream schools. However, the process of facilitating their learning and participation remains a complex and poorly understood area of education. Although many teachers in mainstream schools are firmly committed to the principles of inclusive education, they do not feel that they have the necessary training and support to provide adequately for pupils with ASD. One solution for this problem is to use a virtual tutor to supplement the education of pupils with ASD in mainstream schools. This thesis describes research to develop a Novel Arabic Conversational Intelligent Tutoring System (CITS), called LANA, for children with ASD, which delivers topics related to the science subject by engaging with the user in Arabic language. The Visual, Auditory, and Kinaesthetic (VAK) learning style model is used in LANA to adapt to the children’s learning style by personalising the tutoring session. Development of an Arabic Conversational Agent has many challenges. Part of the challenge in building such a system is the requirement to deal with the grammatical features and the morphological nature of the Arabic language. The proposed novel architecture for LANA uses both pattern matching (PM) and a new Arabic short text similarity (STS) measure to extract facts from user’s responses to match rules in scripted conversation in a particular domain (Science). In this research, two prototypes of an Arabic CITS were developed (LANA-I) and (LANA-II). LANA-I was developed and evaluated with 24 neurotypical children to evaluate the effectiveness and robustness of the system engine. LANA-II was developed to enhance LANA-I by addressing spelling mistakes and words variation with prefix and suffix. Also in LANA-II, TEACCH method was added to the user interface to adapt the tutorial environment to the autistic students learning, and the knowledge base was expanded by adding a new tutorial. An evaluation methodology and experiment were designed to evaluate the enhanced components of LANA-II architecture. The results illustrated a statistically significant impact on the effectiveness of LANA-II engine when compared to LANA-I. In addition, the results indicated a statistically significant improvement on the autistic students learning gain with adapting to their learning styles indicating that LANA-II can be adapted to autistic children’s learning styles and enhance their learning

Gestión de Recursos Radio en Redes Móviles Celulares Basadas en Tecnología OFDMA para la Provisión de QoS y Control de la Interferencia

El trabajo realizado en esta tesis, enmarcado en el contexto de la provisión de QoS en redes móviles de banda ancha, se ha centrado en la propuesta y evaluación de algoritmos de asignación de recursos radio en el enlace descendente para la gestión de la interferencia en redes basadas en tecnología OFDMA. En un contexto de redes móviles de banda ancha en las que los usuarios demandan cada vez servicios más diversos y con requisitos de QoS más heterogéneos, resulta indispensable obtener un aprovechamiento máximo de los recursos radio disponibles en el sistema. Con este fin, la mayor parte de las redes contemplan un despliegue con reúso unidad de modo que los mismos recursos son utilizados en todas las celdas del sistema. En este contexto, interferencia intercelular (ICI) es uno de los factores que más impacto tienen en las prestaciones finales ofrecidas por los sistemas, especialmente para los usuarios situados en la zona exterior de la celda. El problema, lejos de estar resuelto, continúa siendo objeto de estudio pues no existe una solución óptima al mismo y existen un gran número de factores a implicados. El objetivo de esta tesis ha sido definir mecanismos de control de las interferencias intercelulares (en el caso de considerar sistema de reúso frecuencial total a nivel de celda) e intersector (en el caso de considerar reúso unidad en cada sector) que mitigan el efecto de las mismas y mejoran la calidad de la señal recibida por estos usuarios exteriores. Bajo las restricciones definidas por el mecanismo de control de interferencias, se han diseñado algoritmos eficientes para la asignación dinámica de recursos radio dependientes del canal, que aseguren a su vez el cumplimiento de los requisitos de QoS de los distintos flujos de datos