Swarm Intelligence (SI) is a young field of study from which solutions to complex problems have been proposed based on how some natural organisms (e.g. ants, bees and others) achieve many of their daily tasks through simple sets of interactions. This thesis proposes two models for area exploration and coverage based on SI principles. These two models present a novel approach based on the combination of: ants’ pheromones, in order to keep track of visited places; and bird flocks or fish schooling, so as to move and collaborate. An implementation of both models was done in order to simulate and evaluate both the emergent behavior of the agents as well as their area exploration and coverage performance. Based on the outcome of the simulations it is concluded that both models are able to perform the exploration and coverage task and that one model is better than the other.

Scheduling of aperiodic and independent tasks in hard real-time symmetric multiprocessing systems is an NP-complete problem that is often solved using heuristics like particle swarm optimization (PSO). The performance of these class of heuristics, known as evolutionary algorithms, are often evaluated based on the number of iterations it takes to find a solution. Such metrics provide limited information on how the algorithm reaches a solution and how the process could be accelerated. This thesis presents a methodology to analyze the trajectory formed by candidate solutions in order to analyze them in both the time and frequency domains at a single scale. The analysis entails (i) the impact of different parameters for the PSO algorithm, and (ii) the evolutionary processes in the swarm. The work reveals that particles have a directed movement towards a solution during a transient phase, and then enter a steady state where they perform an unguided local search. The scheduling algorithm presented in this thesis uses a variation of the minimum total tardiness with cumulative penalties cost function, that can be extended to suit different system needs. The experimental results show that the scheduler is able to distribute tasks to meet the real-time deadlines over 1, 2, and 4 processors and up to 30 tasks with overall system loads of up to 50\% in fewer than 1,000 iterations. When scheduling greater loads, the scheduler reaches local solutions with 1 to 2 missed deadlines, while larger tasks sets take longer to converge. The trajectories of the particles during the scheduling algorithm are examined as a means to emphasize the impact of the behaviour on the application performance and give insight into ways to improve the algorithm for both space and terrestrial applications.

Network resiliency pertains to the vulnerability of telecommunication networks in the case of failures and malicious attacks. With the increasing capacity catering of network for the booming multi-services in Next Generation Networks (NGNs), reducing recovery time and improving capacity efficiency while providing high quality and resiliency of services has become increasingly important for the future network development. Providing network resiliency means to rapidly and accurately reroute the traffic via diversely routed spare capacity in the network when a failure takes down links or nodes in the working path. Planning and optimization for NGNs require an efficient algorithm for spare capacity allocation (SCA) that assures restorability with a minimum of total capacity. This dissertation aims to understand and advance the state of knowledge on spare capacity allocation in network resiliency for telecommunication core networks. Optimal network resiliency design for restorability requires considering: network topology, working and protection paths routing and spare capacity allocation. Restorable networks should be highly efficient in terms of total capacity required for restorability and be able to support any target level of restorability. The SCA strategy is to decide how much spare capacity should be reserved on links and to pre-plan protection paths to protect traffic from a set of failures. This optimal capacity allocation problem for survivable routing is known as NP-complete. To expose the problem structure, we propose a model of the SCA problem using a matrix-based framework, named Distributed Resilience Matrix (DRM) to identify the dependencies between the working and protection capacities associated with each pair of links and also to capture the local capacity usage information in a distributed control environment. In addition, we introduce a novel ant-based heuristic algorithm, called Friend-or-Foe Resilient (FoF-R) ant-based routing algorithm to find the optimal protection cycle (i.e., two node-disjoint paths between a source-destination node pair) and explore the sharing ability among protection paths using a capacity headroom-dependent attraction and repulsion function. Simulation results based on the OMNeT++ and AMPL/CPLEX tools show that the FoF-R scheme with the DRM structure is a promising approach to solving the SCA problem for survivable routing and it gives a good trade off between solution optimality and computation speed. Furthermore, for the SCA studies of survivable networks, it is also important to be able to differentiate between network topologies by means of a robust numerical measure that indicates the level of immunity of these topologies to failures of their nodes and links. Ideally, such a measure should be sensitive to the existence of nodes or links, which are more important than others, for example, if their failure causes the network’s disintegration. Another contribution in this dissertation is to introduce an algebraic connectivity metric, adopted from the spectral graph theory, namely the 2nd smallest eigenvalue of the Laplacian matrix of the network topology, instead of the average nodal degree, to characterize network robustness in studies of the SCA problem. Extensive simulation studies confirm that this metric is a more informative parameter than the average nodal degree for characterizing network topologies in network resiliency studies.

Scheduling of aperiodic and independent tasks in hard real-time symmetric multiprocessing systems is an NP-complete problem that is often solved using heuristics like particle swarm optimization (PSO). The performance of these class of heuristics, known as evolutionary algorithms, are often evaluated based on the number of iterations it takes to find a solution. Such metrics provide limited information on how the algorithm reaches a solution and how the process could be accelerated. This thesis presents a methodology to analyze the trajectory formed by candidate solutions in order to analyze them in both the time and frequency domains at a single scale. The analysis entails (i) the impact of different parameters for the PSO algorithm, and (ii) the evolutionary processes in the swarm. The work reveals that particles have a directed movement towards a solution during a transient phase, and then enter a steady state where they perform an unguided local search. The scheduling algorithm presented in this thesis uses a variation of the minimum total tardiness with cumulative penalties cost function, that can be extended to suit different system needs. The experimental results show that the scheduler is able to distribute tasks to meet the real-time deadlines over 1, 2, and 4 processors and up to 30 tasks with overall system loads of up to 50\% in fewer than 1,000 iterations. When scheduling greater loads, the scheduler reaches local solutions with 1 to 2 missed deadlines, while larger tasks sets take longer to converge. The trajectories of the particles during the scheduling algorithm are examined as a means to emphasize the impact of the behaviour on the application performance and give insight into ways to improve the algorithm for both space and terrestrial applications.

Thesis (Ph. D.)--University of Missouri-Columbia, 2007. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed Mar. 3, 2008). Vita. Includes bibliographical references.

Thesis (M.S.)--Missouri University of Science and Technology, 2010. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed April 8, 2010) Includes bibliographical references (p. 76-78).

Thesis (Ph. D.)-- State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Engineering, 2009.

Population-based global optimization algorithms including Particle Swarm Optimization (PSO) have become popular for solving multi-optima problems much more efficiently than the traditional mathematical techniques. In this research, we present and evaluate a new parallel PSO algorithm that provides a significant performance improvement as compared to the serial PSO algorithm. Instead of merely assigning parts of the task of serial version to several processors, the new algorithm places multiple swarms on the available nodes in which operate independently, while collaborating on the same task. With the reduction of the communication bottleneck as well the ability to manipulate the individual swarms independently, the proposed approach outperforms the original PSO algorithm and still maintains the simplicity and ease of implementation.

This research presents various intelligent optimisation methods which are: genetic algorithm (GA), particle swarm optimisation (PSO), artificial bee colony algorithm (ABCA), firefly algorithm (FA) and bacterial foraging optimisation (BFO). It attempts to minimise analogue electronic filter and amplifier circuits, taking a cascode amplifier design as a case study, and utilising the above-mentioned intelligent optimisation algorithms with the aim of determining the best among them to be used. Small signal analysis (SSA) conversion of the cascode circuit is performed while mesh analysis is applied to transform the circuit to matrices form. Computer programmes are developed in Matlab using the above mentioned intelligent optimisation algorithms to minimise the cascode amplifier circuit. The objective function is based on input resistance, output resistance, power consumption, gain, upperfrequency band and lower frequency band. The cascode circuit result presented, applied the above-mentioned existing intelligent optimisation algorithms to optimise the same circuit and compared the techniques with the one using Nelder-Mead and the original circuit simulated in PSpice. Four circuit element types (resistors, capacitors, transistors and operational amplifier (op-amp)) are targeted using the optimisation techniques and subsequently compared to the initial circuit. The PSO based optimised result has proven to be best followed by that of GA optimised technique regarding power consumption reduction and frequency response. This work modifies symbolic circuit analysis in Matlab (MSCAM) tool which utilises Netlist from PSpice or from simulation to generate matrices. These matrices are used for optimisation or to compute circuit parameters. The tool is modified to handle both active and passive elements such as inductors, resistors, capacitors, transistors and op-amps. The transistors are transformed into SSA and op-amp use the SSA that is easy to implement in programming. Results are presented to illustrate the potential of the algorithm. Results are compared to PSpice simulation and the approach handled larger matrices dimensions compared to that of existing symbolic circuit analysis in Matlab tool (SCAM). The SCAM formed matrices by adding additional rows and columns due to how the algorithm was developed which takes more computer resources and limit its performance. Next to this, this work attempts to reduce component count in high-pass, low-pass, and all- pass active filters. Also, it uses a lower order filter to realise same results as higher order filter regarding frequency response curve. The optimisers applied are GA, PSO (the best two methods among them) and Nelder-Mead (the worst method) are used subsequently for the filters optimisation. The filters are converted into their SSA while nodal analysis is applied to transform the circuit to matrices form. High-pass, low-pass, and all- pass active filters results are presented to demonstrate the effectiveness of the technique. Results presented have shown that with a computer code, a lower order op-amp filter can be applied to realise the same results as that of a higher order one. Furthermore, PSO can realise the best results regarding frequency response for the three results, followed by GA whereas Nelder- Mead has the worst results. Furthermore, this research introduced genetic folding (GF), MSCAM, and automatically simulated Netlist into existing genetic programming (GP), which is a new contribution in this work, which enhances the development of independent Matlab toolbox for the evolution of passive and active filter circuits. The active filter circuit evolution especially when operational amplifier is involved as a component is of it first kind in circuit evolution. In the work, only one software package is used instead of combining PSpice and Matlab in electronic circuit simulation. This saves the elapsed time for moving the simulation between the two platforms and reduces the cost of subscription. The evolving circuit from GP using Matlab simulation is automatically transformed into a symbolic Netlist also by Matlab simulation. The Netlist is fed into MSCAM; where MSCAM uses it to generate matrices for the simulation. The matrices enhance frequency response analysis of low-pass, high-pass, band-pass, band-stop of active and passive filter circuits. After the circuit evolution using the developed GP, PSO is then applied to optimise some of the circuits. The algorithm is tested with twelve different circuits (five examples of the active filter, four examples of passive filter circuits and three examples of transistor amplifier circuits) and the results presented have shown that the algorithm is efficient regarding design.

CAPES / Este trabalho apresenta a aplicação dos métodos de otimização por enxame de partículas e por colônia de formigas na otimização dos parâmetros de um controlador não linear de estrutura variável baseado em um controlador de variância mínima generalizada. Este controlador é composto por duas parcelas distintas: uma parcela linear e outra não linear. A parcela não linear do controlador apresenta dois parâmetros que afeta diretamente o comportamento do controlador e tais parâmetros são obtidos de maneira empírica. As metaheurísticas foram aplicadas para se obter os valores otimizados destes parâmetros. Foi considerada uma função custo que leva em consideração o erro de rastreamento e a variação da ação de controle. Um exemplo numérico do projeto deste controlador também é apresentado. O controlador otimizado foi experimentado em três plantas reais: controle de velocidade de um servomecanismo, controle de nível e controle de vazão em uma planta didática industrial. Os resultados obtidos enfatizam a melhora do desempenho do controlador com os parâmetros otimizados. Também é apresentada a comparação do desempenho deste controlador com um controlador PI. / This work presents the application of particle swarm optimization and ant colony optimization in the parameters optimization of a non-linear controller with variable structure based on generalized minimum variance control. This controller is composed of two parts: linear and non-linear. The non-linear term of the controller consists of two parameters that directly affects the control action, and are obtained by trial and error. Metaheuristic methods were applied to find out the optimized values of these parameters. The cost function used in metaheuristic methods takes account the error and the control signal. A numerical example of the design of this controller is also presented. Three practical experiments were considered: a servomechanism velocity control and two control loops in a didactic industrial plant, level and flow control. Experimental results emphasize the improvement of the system performance when the optimization methods are applied. A comparision with PI controller is shown.