Digital Filter Design Using Improved Artificial Bee Colony Algorithms

Digital filters are often used in digital signal processing applications. The design objective of a digital filter is to find the optimal set of filter coefficients, which satisfies the desired specifications of magnitude and group delay responses. Evolutionary algorithms are population-based meta-heuristic algorithms inspired by the biological behaviors of species. Compared to gradient-based optimization algorithms such as steepest descent and Newton’s like methods, these bio-inspired algorithms have the advantages of not getting stuck at local optima and being independent of the starting point in the solution space. The limitations of evolutionary algorithms include the presence of control parameters, problem specific tuning procedure, premature convergence and slower convergence rate. The artificial bee colony (ABC) algorithm is a swarm-based search meta-heuristic algorithm inspired by the foraging behaviors of honey bee colonies, with the benefit of a relatively fewer control parameters. In its original form, the ABC algorithm has certain limitations such as low convergence rate, and insufficient balance between exploration and exploitation in the search equations. In this dissertation, an ABC-AMR algorithm is proposed by incorporating an adaptive modification rate (AMR) into the original ABC algorithm to increase convergence rate by adjusting the balance between exploration and exploitation in the search equations through an adaptive determination of the number of parameters to be updated in every iteration. A constrained ABC-AMR algorithm is also developed for solving constrained optimization problems.There are many real-world problems requiring simultaneous optimizations of more than one conflicting objectives. Multiobjective (MO) optimization produces a set of feasible solutions called the Pareto front instead of a single optimum solution. For multiobjective optimization, if a decision maker’s preferences can be incorporated during the optimization process, the search process can be confined to the region of interest instead of searching the entire region. In this dissertation, two algorithms are developed for such incorporation. The first one is a reference-point-based MOABC algorithm in which a decision maker’s preferences are included in the optimization process as the reference point. The second one is a physical-programming-based MOABC algorithm in which physical programming is used for setting the region of interest of a decision maker. In this dissertation, the four developed algorithms are applied to solve digital filter design problems. The ABC-AMR algorithm is used to design Types 3 and 4 linear phase FIR differentiators, and the results are compared to those obtained by the original ABC algorithm, three improved ABC algorithms, and the Parks-McClellan algorithm. The constrained ABC-AMR algorithm is applied to the design of sparse Type 1 linear phase FIR filters of filter orders 60, 70 and 80, and the results are compared to three state-of-the-art design methods. The reference-point-based multiobjective ABC algorithm is used to design of asymmetric lowpass, highpass, bandpass and bandstop FIR filters, and the results are compared to those obtained by the preference-based multiobjective differential evolution algorithm. The physical-programming-based multiobjective ABC algorithm is used to design IIR lowpass, highpass and bandpass filters, and the results are compared to three state-of-the-art design methods. Based on the obtained design results, the four design algorithms are shown to be competitive as compared to the state-of-the-art design methods

Multiobjective differential evolution based on fuzzy performance feedback: Soft constraint handling and its application in antenna designs

The recently emerging Differential Evolution is considered one of the most powerful tools for solving optimization problems. It is a stochastic population-based search approach for optimization over the continuous space. The main advantages of differential evolution are simplicity, robustness and high speed of convergence. Differential evolution is attractive to researchers all over the world as evidenced by recent publications. There are many variants of differential evolution proposed by researchers and differential evolution algorithms are continuously improved in its performance. Performance of differential evolution algorithms depend on the control parameters setting which are problem dependent and time-consuming task. This study proposed a Fuzzy-based Multiobjective Differential Evolution (FMDE) that exploits three performance metrics, specifically hypervolume, spacing, and maximum spread, to measure the state of the evolution process. We apply the fuzzy inference rules to these metrics in order to adaptively adjust the associated control parameters of the chosen mutation strategy used in this algorithm. The proposed FMDE is evaluated on the well known ZDT, DTLZ, and WFG benchmark test suites. The experimental results show that FMDE is competitive with respect to the chosen state-of-the-art multiobjective evolutionary algorithms. The advanced version of FMDE with adaptive crossover rate (AFMDE) is proposed. The proof of concept AFMDE is then applied specifically to the designs of microstrip antenna array. Furthermore, the soft constraint handling technique incorporates with AFMDE is proposed. Soft constraint AFMDE is evaluated on the benchmark constrained problems. AFMDE with soft constraint handling technique is applied to the constrained non-uniform circular antenna array design problem as a case study

Multidisciplinary Design Optimization for Space Applications

Multidisciplinary Design Optimization (MDO) has been increasingly studied in aerospace engineering with the main purpose of reducing monetary and schedule costs. The traditional design approach of optimizing each discipline separately and manually iterating to achieve good solutions is substituted by exploiting the interactions between the disciplines and concurrently optimizing every subsystem. The target of the research was the development of a flexible software suite capable of concurrently optimizing the design of a rocket propellant launch vehicle for multiple objectives. The possibility of combining the advantages of global and local searches have been exploited in both the MDO architecture and in the selected and self developed optimization methodologies. Those have been compared according to computational efficiency and performance criteria. Results have been critically analyzed to identify the most suitable optimization approach for the targeted MDO problem

Adaptive algorithms for history matching and uncertainty quantification

Numerical reservoir simulation models are the basis for many decisions in regard to predicting, optimising, and improving production performance of oil and gas reservoirs. History matching is required to calibrate models to the dynamic behaviour of the reservoir, due to the existence of uncertainty in model parameters. Finally a set of history matched models are used for reservoir performance prediction and economic and risk assessment of different development scenarios. Various algorithms are employed to search and sample parameter space in history matching and uncertainty quantification problems. The algorithm choice and implementation, as done through a number of control parameters, have a significant impact on effectiveness and efficiency of the algorithm and thus, the quality of results and the speed of the process. This thesis is concerned with investigation, development, and implementation of improved and adaptive algorithms for reservoir history matching and uncertainty quantification problems. A set of evolutionary algorithms are considered and applied to history matching. The shared characteristic of applied algorithms is adaptation by balancing exploration and exploitation of the search space, which can lead to improved convergence and diversity. This includes the use of estimation of distribution algorithms, which implicitly adapt their search mechanism to the characteristics of the problem. Hybridising them with genetic algorithms, multiobjective sorting algorithms, and real-coded, multi-model and multivariate Gaussian-based models can help these algorithms to adapt even more and improve their performance. Finally diversity measures are used to develop an explicit, adaptive algorithm and control the algorithm’s performance, based on the structure of the problem. Uncertainty quantification in a Bayesian framework can be carried out by resampling of the search space using Markov chain Monte-Carlo sampling algorithms. Common critiques of these are low efficiency and their need for control parameter tuning. A Metropolis-Hastings sampling algorithm with an adaptive multivariate Gaussian proposal distribution and a K-nearest neighbour approximation has been developed and applied

Stochastic and deterministic algorithms for continuous black-box optimization

Continuous optimization is never easy: the exact solution is always a luxury demand and the theory of it is not always analytical and elegant. Continuous optimization, in practice, is essentially about the efficiency: how to obtain the solution with same quality using as minimal resources (e.g., CPU time or memory usage) as possible? In this thesis, the number of function evaluations is considered as the most important resource to save. To achieve this goal, various efforts have been implemented and applied successfully. One research stream focuses on the so-called stochastic variation (mutation) operator, which conducts an (local) exploration of the search space. The efficiency of those operator has been investigated closely, which shows a good stochastic variation should be able to generate a good coverage of the local neighbourhood around the current search solution. This thesis contributes on this issue by formulating a novel stochastic variation that yields good space coverage. Algorithms and the Foundations of Software technolog

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Designing screws for polymer compounding in twin-screw extruders

Tese de doutoramento em Ciência e Engenharia de Polímeros e CompósitosConsidering its modular construction, co-rotating twin screw extruders can be easily adapted to work with polymeric systems with more stringent specifications. However, their geometrical flexibility makes the performance of these machines strongly dependent on the screw configuration. Therefore, the definition of the adequate screw geometry to use in a specific polymer system is an important process requirement which is currently achieved empirically or using a trial-and-error basis. The aim of this work is to develop an automatic optimization methodology able to define the best screw geometry/configuration to use in a specific compounding/reactive extrusion operation, reducing both cost and time. This constitutes an optimization problem where a set of different screw elements are to be sequentially positioned along the screw in order to maximize the extruder performance. For that, a global modeling program considering the most important physical, thermal and rheological phenomena developing along the axis of an intermeshing co-rotating twin screw extruder was initially developed. The accuracy and sensitivity of the software to changes in the input parameters was tested for different operating conditions and screw configurations using a laboratorial Leistritz LSM 30.34 extruder. Then, this modeling software was integrated into an optimization methodology in order to be possible solving the Twin Screw Configuration Problem. Multi-objective versions of local search algorithms (Two Phase Local Search and Pareto Local Search) and Ant Colony Optimization algorithms were implemented and adapted to deal with the combinatorial, discrete and multi-objective nature of the problem. Their performance was studied making use of the hypervolume indicator and Empirical Attainment Function, and compared with the Reduced Pareto Search Genetic Algorithm (RPSGA) previously developed and applied to this problem. In order to improve the quality of the results and/or to decrease the computational cost required by the optimization methodology, different hybrid algorithms were tested. The approaches developed considers the use of local search procedures (TPLS and PLS algorithms) into population based metaheuristics, as MOACO and MOEA algorithms. Finally, the optimization methodology developed was applied to the optimization of a starch cationization reaction. Several starch cationization case studies, involving different screw elements screw lengths and conflicting objectives, were tested in order to validate this technique and to prove the potential of this automatic optimization methodology.Devido à sua construção modular, as extrusoras de duplo-fuso co-rotativas podem ser facilmente adaptadas a sistemas poliméricos que requerem especificações mais rigorosas. No entanto, esta flexibilidade geométrica torna o seu desempenho fortemente dependente da configuração do parafuso. Por isso, a tarefa de definir a melhor configuração do parafuso para usar num determinado sistema polimérico é um requisito importante do processo que é actualmente realizada empiricamente ou utilizando um processo de tentativa erro. O objectivo principal deste trabalho é desenvolver uma metodologia automática de optimização que seja capaz de definir a melhor configuração/geometria do parafuso a usar num determinado sistema de extrusão, reduzindo custos e tempo. Este problema é um problema de optimização, onde os vários elementos do parafuso têm que ser sequencialmente posicionados ao longo do eixo do parafuso de forma a maximizar o desempenho da extrusora. Para isso, foi inicialmente desenvolvido um programa de modelação que considera os mais importantes fenómenos físicos, térmicos e reológicos que ocorrem ao longo da extrusora de duplo fuso co-rotativa. De forma a testar a precisão e a sensibilidade do software às alterações dos parâmetros, diversas condições operativas e configurações de parafuso foram testadas tendo como base uma extrusora laboratorial Leistritz LSM 30.34. Seguidamente, este software de modelação foi integrado numa metodologia de optimização com vista à resolução do problema de configuração da extrusora de duplo-fuso. Para lidar com a natureza combinatorial, discreta e multi-objectiva do problema em estudo, foram adaptadas e implementadas versões multi-objectivas de algoritmos de procura local (Two-Phase Local Search and Pareto Local Search) e Ant Colony Optimization. O desempenho dos diversos algoritmos foi estudado usando o hipervolume e as Empirical Attainment Functions. Os resultados foram comparados com os resultados obtidos com o algoritmo genético Reduced Pareto Search Genetic Algorithm (RPSGA) desenvolvido e aplicado anteriormente a este problema. Com o objectivo de melhorar a qualidade dos resultados e/ou diminuir o esforço computacional exigido pela metodologia de optimização, foram testadas diversas hibridizações. Os algoritmos híbridos desenvolvidos consideram a integração de algoritmos de procura local (TPLS e PLS) noutras metheuristicas, como MOACO e MOEA. Por fim, a metodologia de optimização desenvolvida neste trabalho foi testada na optimização de uma reacção de cationização do amido. Para validar esta técnica e provar o seu potencial, foram realizados vários estudos envolvendo diferentes elementos e comprimentos de parafusos, bem como, a optimização de objectivos em conflito