Evolving Combinational Logic Circuits Using a Hybrid Quantum Evolution and Particle Swarm Inspired Algorithm

In this paper, an algorithm inspired from quantum evolution and particle swarm to evolve combinational logic circuits is presented. This algorithm uses the framework of the local version of particle swarm optimization with quantum evolutionary algorithms, and integer encoding. A multi-objective fitness function is used to evolve the combinational logic circuits in order obtain feasible circuits with minimal number of gates in the design. A comparative study indicates the superior performance of the hybrid quantum evolution-particle swarm inspired algorithm over the particle swarm and other evolutionary algorithms (such as genetic algorithms) independently

Optimal SVM Switching for a Multilevel Multi-Phase Machine using Modified Discrete PSO

This paper searches for the best possible switching sequence in a multilevel multi-phase inverter that gives the lowest amount of voltage harmonics. A modified discrete particle swarm (MDPSO) algorithm is used in an attempt to find the optimal space vector modulation switching sequence that results in the lowest voltage THD. As with typical PSO cognitive and social parameters are used to guide the search, but an additional mutation term is added to broaden the amount of area searched. The search space is the feasible solutions for the predetermined vectors at a given modulation index. Comparison of the MDPSO algorithm to an integer particle swarm optimization (IPSO) is presented for all three modulation indices tested. The resulting switching sequences found show that the MDPSO algorithm is capable of finding a minimal THD solution for all modulations indices tested. The MDPSO algorithm performed better overall than the IPSO in terms of converging to the best solution with significantly lower iterations

Design and implementation of a genetic algorithm with integer number coding for the evolution of FPGAs in space applications

In [1] a form of representation of logic circuits by chains of integer numbers is presented. That type of representation is easy to simulate and to export to FPGA hardware in such a way that, by adding a genetic algorithm (GA), it can be used in an evolutionary process. Because regular GAs utilize binary number coding, one was designed, with all its operators and processes, that uses integer number coding. This evolvable hardware (EH) process was tested with more than 200 hours of runs to determine the effectiveness of the integer coded GA. Results show that, given the proper conditions, the GA is effective in finding solutions that fulfill the required needs of the target system and that this particular EH platform is suitable for applications where fault tolerance capability is required, such as space systems.Eje: Workshop Arquitectura, redes y sistemas operativos (WARSO)Red de Universidades con Carreras en Informática (RedUNCI

Intelligent optimisation of analogue circuits using particle swarm optimisation, genetic programming and genetic folding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.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.Tertiary Education Trust Fund (TETFUND) through University of Calabar, Nigeria

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Physically-Adaptive Computing via Introspection and Self-Optimization in Reconfigurable Systems.

Digital electronic systems typically must compute precise and deterministic results, but in principle have flexibility in how they compute. Despite the potential flexibility, the overriding paradigm for more than 50 years has been based on fixed, non-adaptive inte-grated circuits. This one-size-fits-all approach is rapidly losing effectiveness now that technology is advancing into the nanoscale. Physical variation and uncertainty in com-ponent behavior are emerging as fundamental constraints and leading to increasingly sub-optimal fault rates, power consumption, chip costs, and lifetimes. This dissertation pro-poses methods of physically-adaptive computing (PAC), in which reconfigurable elec-tronic systems sense and learn their own physical parameters and adapt with fine granu-larity in the field, leading to higher reliability and efficiency. We formulate the PAC problem and provide a conceptual framework built around two major themes: introspection and self-optimization. We investigate how systems can efficiently acquire useful information about their physical state and related parameters, and how systems can feasibly re-implement their designs on-the-fly using the information learned. We study the role not only of self-adaptation—where the above two tasks are performed by an adaptive system itself—but also of assisted adaptation using a remote server or peer. We introduce low-cost methods for sensing regional variations in a system, including a flexible, ultra-compact sensor that can be embedded in an application and implemented on field-programmable gate arrays (FPGAs). An array of such sensors, with only 1% to-tal overhead, can be employed to gain useful information about circuit delays, voltage noise, and even leakage variations. We present complementary methods of regional self-optimization, such as finding a design alternative that best fits a given system region. We propose a novel approach to characterizing local, uncorrelated variations. Through in-system emulation of noise, previously hidden variations in transient fault sus-ceptibility are uncovered. Correspondingly, we demonstrate practical methods of self-optimization, such as local re-placement, informed by the introspection data. Forms of physically-adaptive computing are strongly needed in areas such as com-munications infrastructure, data centers, and space systems. This dissertation contributes practical methods for improving PAC costs and benefits, and promotes a vision of re-sourceful, dependable digital systems at unimaginably-fine physical scales.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78922/1/kzick_1.pd

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

Computer Science & Technology Series : XVIII Argentine Congress of Computer Science. Selected papers

CACIC’12 was the eighteenth Congress in the CACIC series. It was organized by the School of Computer Science and Engineering at the Universidad Nacional del Sur. The Congress included 13 Workshops with 178 accepted papers, 5 Conferences, 2 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. CACIC 2012 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 302 submissions. An average of 2.5 review reports were collected for each paper, for a grand total of 752 review reports that involved about 410 different reviewers. A total of 178 full papers, involving 496 authors and 83 Universities, were accepted and 27 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI