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This thesis deals with various facets of the optimisation problem for telecommunication networks and proposes a number of new techniques for their solution. The necessary essentials, Graph Theory, Complexity Theory and Telecommunication Principles, are investigated. The relevant graphs are enumerated and the requirements of suitable optimisation algorithms for certain graphical problems are established. The Private Automatic Branch Exchange (PABX) is introduced. the variety of telecommunications traffic as well as the practical requirements of a connection topology are discussed. The fundamental Network Optimisation Problem (NJP) is defined and analysed. Simple exhaustive methods of solution are considered together with partial solution algorithms and simplification methods. Centralised networks with and without concentrators are introduced. Extensions and modifications are proposed for some techniques and existing practical methods of dealing with the NOP are investigated. A number of new ideas are proposed for the practical solution of the NOP. Reduction methods are presented for replacing large unmanageable networks with smaller ones, on which optimisation can take place. Fixed topology techniques are introduced for initial tandem switch selection purposes and perturbation methods are considered which can be applied to such an initial solution. Lookahead methods of link removal are introduced for the purposes of determining the tandem interconnection network together with the traffic routeing strategy. A composite method is proposed incorporating all of these concepts and the results of a number of numerical experiments upon actual network problem; are presented. the extension of the proposed techniques to other areas of problem solving and optimisation is considered. In particular, a new method for the solution of the Euclidean Travelling Salesman Problem (ETSP) is presented. A brief discussion is undertaken, in conclusion, concerning the practical difficulties of the NOP and The restrictions this placed upon solution algorithms of various types.

Combinatorial optimisation problems (COPs) have been at the origin of the design of many optimal and heuristic solution frameworks such as branch-and-bound algorithms, branch-and-cut algorithms, classical local search methods, metaheuristics, and hyperheuristics. This thesis proposes a refined generic and parametrised infeasible local search (GPILS) algorithm for solving COPs and customises it to solve the traveling salesman problem (TSP), for illustration purposes. In addition, a rule-based heuristic is proposed to initialise infeasible local search, referred to as the parameterised infeasible heuristic (PIH), which allows the analyst to have some control over the features of the infeasible solution he/she might want to start the infeasible search with. A recursive infeasible neighbourhood search (RINS) as well as a generic patching procedure to search the infeasible space are also proposed. These procedures are designed in a generic manner, so they can be adapted to any choice of parameters of the GPILS, where the set of parameters, in fact for simplicity, refers to set of parameters, components, criteria and rules. Furthermore, a hyperheuristic framework is proposed for optimizing the parameters of GPILS referred to as HH-GPILS. Experiments have been run for both sequential (i.e. simulated annealing, variable neighbourhood search, and tabu search) and parallel hyperheuristics (i.e., genetic algorithms / GAs) to empirically assess the performance of the proposed HH-GPILS in solving TSP using instances from the TSPLIB. Empirical results suggest that HH-GPILS delivers an outstanding performance. Finally, an offline learning mechanism is proposed as a seeding technique to improve the performance and speed of the proposed parallel HH-GPILS. The proposed offline learning mechanism makes use of a knowledge-base to keep track of the best performing chromosomes and their scores. Empirical results suggest that this learning mechanism is a promising technique to initialise the GA's population.

Combinatorial optimisation problems (COPs) pervade human society: scheduling, design, layout, distribution, timetabling, resource allocation and project management all feature problems where the solution is some combination of elements, the overall value of which needs to be either maximised or minimised (i.e., optimised), typically subject to a number of constraints. Thus, techniques to efficiently solve such problems are an important area of research. A popular group of optimisation algorithms are the metaheuristics, approaches that specify how to search the space of solutions in a problem independent way so that high quality solutions are likely to result in a reasonable amount of computational time. Although metaheuristic algorithms are specified in a problem independent manner, they must be tailored to suit each particular problem to which they are applied. This thesis investigates a number of aspects of the application of the relatively new Ant Colony Optimisation (ACO) metaheuristic to different COPs.The standard ACO metaheuristic is a constructive algorithm loosely based on the foraging behaviour of ant colonies, which are able to find the shortest path to a food source by indirect communication through pheromones. ACO’s artificial pheromone represents a model of the solution components that its artificial ants use to construct solutions. Developing an appropriate pheromone representation is a key aspect of the application of ACO to a problem. An examination of existing ACO applications and the constructive approach more generally reveals how the metaheuristic can be applied more systematically across a range of COPs. The two main issues addressed in this thesis are biases inherent in the constructive process and the systematic selection of pheromone representations.The systematisation of ACO should lead to more consistently high performance of the algorithm across different problems. Additionally, it supports the creation of a generalised ACO system, capable of adapting itself to suit many different combinatorial problems without the need for manual intervention.

There are many real world assignment, scheduling and planning tasks which can be classified as combinatorial optimisation problems (COPs). These are usually formulated as a mathematical problem of minimising or maximising some cost function subject to a number of constraints. Usually, such problems are NP hard, and thus, whilst it is possible to find exact solutions to specific problems, in general only approximate solutions can be found. There are many algorithms that have been proposed for finding approximate solutions to COPs, ranging from special purpose heuristics to general search meta-heuristics such as simulated annealing and tabu search. General meta-heuristic algorithms like simulated annealing have been applied to a wide range of problems. In most cases, the designer must choose an appropriate data structure and a set of local operators that define a search neighbourhood. The variability in representation techniques, and suitable neighbourhood transition operators, has meant that it is usually necessary to develop new code for each problem. Toolkits like the one developed by Ingber's Adaptive Simulated Annealing (Ingber 1993, 1996) have been applied to assist rapid prototyping of simulated annealing codes, however, these still require the development of new programs for each type of problem. There have been very few attempts to develop a general meta-heuristic solver, with the notable exception being Connolly's General Purpose Simulated Annealing (Connolly 1992). In this research, a general meta-heuristic based system is presented that is suitable for a wide range of COPs. The main goal of this work is to build an environment in which it is possible to specify a range of COPs using an algebraic formulation, and to produce a tailored solver automatically. This removes the need for the development of specific software, allowing very rapid prototyping. Similar techniques have been available for linear programming based solvers for some years in the form of the GAMS (General Algebraic Modelling System) (Brooke, Kendrick, Meeraus and Raman 1997) and AMPL (Fourer, Gay and Kernighan 1993) interfaces. The new system is based on a novel linked list data structure rather than the more conventional vector notation due to the natural mapping between COPS and lists. In addition, the modelling system is found to be very suitable for processing by meta-heuristic search algorithms as it allows the direct application of common local search operators. A general solver is built that is based on the linked list modelling system. This system is capable of using meta-heuristic search engines such as greedy search, tabu search and simulated annealing. A number of implementation issues such as generating initial solutions, choosing and invoking appropriate local search transition operators and producing suitable incremental cost expressions, are considered. As such, the system can been seen as a good test-bench for model prototypers and those who wish to test various meta-heuristic implementations in a standard way. However, it is not meant as a replacement or substitute for efficient special purpose search algorithms. The solver shows good performance on a wide range of problems, frequently reaching the optimal and best-known solutions. Where this is not the case, solutions within a few percent deviation are produced. Performance is dependent on the chosen transition operators and the frequency with which each is applied. To a lesser extent, the performance of this implementation is influenced by runtime parameters of the meta-heuristic search engine.

Combinatorial optimisation problems (COPs) pervade human society: scheduling, design, layout, distribution, timetabling, resource allocation and project management all feature problems where the solution is some combination of elements, the overall value of which needs to be either maximised or minimised (i.e., optimised), typically subject to a number of constraints. Thus, techniques to efficiently solve such problems are an important area of research. A popular group of optimisation algorithms are the metaheuristics, approaches that specify how to search the space of solutions in a problem independent way so that high quality solutions are likely to result in a reasonable amount of computational time. Although metaheuristic algorithms are specified in a problem independent manner, they must be tailored to suit each particular problem to which they are applied. This thesis investigates a number of aspects of the application of the relatively new Ant Colony Optimisation (ACO) metaheuristic to different COPs.The standard ACO metaheuristic is a constructive algorithm loosely based on the foraging behaviour of ant colonies, which are able to find the shortest path to a food source by indirect communication through pheromones. ACO’s artificial pheromone represents a model of the solution components that its artificial ants use to construct solutions. Developing an appropriate pheromone representation is a key aspect of the application of ACO to a problem. An examination of existing ACO applications and the constructive approach more generally reveals how the metaheuristic can be applied more systematically across a range of COPs. The two main issues addressed in this thesis are biases inherent in the constructive process and the systematic selection of pheromone representations.The systematisation of ACO should lead to more consistently high performance of the algorithm across different problems. Additionally, it supports the creation of a generalised ACO system, capable of adapting itself to suit many different combinatorial problems without the need for manual intervention.

In this thesis, we will discuss the construction of algorithms for solving Constraint Satisfaction Problems (CSPs), and describe two new ways of approaching them. Both approaches are based on the idea that it is sometimes faster to solve a large number of restricted problems than a single, large, problem. One of the strong points of these methods is that the intuition behind them is fairly simple, which is a definite advantage over many techniques currently in use. The first method, the covering method, can be described as follows: We want to solve a CSP with n variables, each having a domain with d elements. We have access to an algorithm which solves problems where the domain has size e < d, and we want to cover the original problem using a number of restricted instances, in such a way that the solution set is preserved. There are two ways of doing this, depending on the amount of work we are willing to invest; either we construct an explicit covering and end up with a deterministic algorithm for the problem, or we use a probabilistic reasoning and end up with a probabilistic algorithm. The second method, called the partitioning method, relaxes the demand on the underlying algorithm. Instead of having a single algorithm for solving problems with domain less than d, we allow an arbitrary number of them, each solving the problem for a different domain size. Thus by splitting, or partitioning, the domain of the large problem, we again solve a large number of smaller problems before arriving at a solution. Armed with these new techniques, we study a number of different problems; the decision problems (d, l)-CSP and k-Colourability, together with their counting counterparts, as well as the optimisation problems Max Ind CSP, Max Value CSP, Max CSP, and Max Hamming CSP. Among the results, we find a very fast, polynomial space algorithm for determining k-colourability of graphs.

Made available in DSpace on 2016-04-27T16:58:20Z (GMT). No. of bitstreams: 1 armando.pdf: 485665 bytes, checksum: e5f3992597d7f0fffbdcf84e5f0e6a67 (MD5) Previous issue date: 2002-11-21 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / In light of the emphasis that the term problem-solving has received in the mathematics education community as well as research results indicating the various difficulties students exhibit in solving problems, we embarked on this study. The study aims to investigate whether students, at the end of the Ensino Médio (High School) are able to resolve optimisation problems. For all of the problems investigated, it was possible to obtain the solution by applying concepts and procedures already studied by the students, among them, systems of inequalities of the first degree. To test our hypothesis that some students would experience difficulties in resolving these problems, we applied a diagnostic test to a group of 33 students. Analysis of students' responses indicated that none of the students were able to resolve the optimisation problems. Our next step was to investigate if, as proposed by Duval (1993), activities concerning the treatment, the conversion and the co-ordination between registers of representation of a certain object contribute to the processes of learning and teaching this object. To this end, we designed a didactic (teaching) sequence. After a second group of 10 students worked through the sequence, they completed a post-test. We conducted a comparative analysis between the responses of the first group to the diagnostic test and the post-test responses given by the second group of students. This analysis showed that, while the first group of students failed to solved the optimisation problems, all the students in the second group approached the problems and most were able to resolve them. These results suggest that the activities of treatment, conversion and co-ordination of registers of representation of the mathematical object system of inequality make an important contribution to the formation of the concept and its application in the resolution of optimisation problems / Em vista do destaque que o termo resolução de problema tem tido na Educação Matemática e de algumas dificuldades dos alunos em resolvê-los, iniciamos essa pesquisa investigando se os alunos que estão terminando o Ensino Médio resolvem alguns problemas de programação linear que podem ser solucionados com conceitos e procedimentos já estudados, entre eles o sistema de inequações do 1º grau. Tendo como hipótese que alguns alunos teriam dificuldades em resolver esses problemas, fizemos um teste diagnóstico para confirmar a nossa hipótese. Depois de confirmada a hipótese e tendo como questão de pesquisa observar se, como proposta por Duval (1993), as atividades que consideram o tratamento, a conversão e a coordenação entre os registros de representação de um determinado objeto contribuem no processo ensino-aprendizagem desse objeto, elaboramos uma seqüência didática. Após o desenvolvimento dessa seqüência-didática, em uma outra turma da 3ª série, aplicamos o pós-teste e fizemos uma análise comparativa entre o teste diagnóstico da primeira turma e o pós-teste aplicado na segunda turma. Essa análise nos evidenciou que, enquanto os alunos da primeira turma não obtiveram sucesso na resolução dos problemas de programação linear e somente resolveram corretamente algumas das atividades sobre inequações do 1º grau, os alunos da segunda turma abordaram os problemas e a maioria deles obtiveram sucesso na resolução. Sendo assim pudemos inferir que as atividades de tratamento, conversão e coordenação dos registros de representação do objeto matemático sistema de inequações, trazem uma importante contribuição para a formação do conceito e a aplicação dele na resolução de problemas de programação linear

No / Evolutionary membrane computing is an important research direction of membrane computing that aims to explore the complex interactions between membrane computing and evolutionary computation. These disciplines are receiving increasing attention. In this paper, an overview of the evolutionary membrane computing state-of-the-art and new results on two established topics in well defined scopes (membrane-inspired evolutionary algorithms and automated design of membrane computing models) are presented. We survey their theoretical developments and applications, sketch the differences between them, and compare the advantages and limitations. (C) 2014 Elsevier Inc. All rights reserved.