A parallel tabu search algorithm for digital filter design

This work presents a parallel object-oriented tabu search (TS) algorithm for static task scheduling. The scheduling problem and the TS method are separately modeled under an object-oriented approach. The TS parallelization follows a strategy based on multi-search threads and the algorithm is fully implemented using the Java language. Besides providing a new scheduling algorithm, this work contributes to demonstrate: the strength of object-orientation also in this field of applications; the adaptability of TS to asynchronous parallelization; the significance of diversification in TS algorithms; and the potential of the Java language in implementing highly portable object-oriented parallel software.

A parallel tabu search (PTS) algorithm for solving ramp rate constrained economic dispatch (CED) problems for generating units with non-monotonically and monotonically increasing incremental cost (IC) functions is proposed. To parallelise tabu search (TS) algorithms efficiently, the neighbourhood decomposition is used to balance the computing load, whereas competitive selection is used to update the best solution reached among subneighbourhoods. The proposed PTS is implemented on a 32-processor Beowulf cluster with an Ethernet switching network on a generating unit system size in the range 10-80 units over the entire dispatch periods. With different subneighbourhood sizes, the proposed PTS compromises the experimental speedup and solution quality for the best performance. PTS is potentially viable for the online implementation of CED because of the substantial generator fuel cost savings and high speedup upper bounds.

This paper proposes an observer-based fault detection and diagnosis scheme for a continuous-time dynamic system with multiple sensors. The optimal finite impulse response (FIR) filter is adopted as an observer. The proposed scheme consists of the main optimal FIR filter and a bank of additional optimal FIR filters whose number is that of observations. We show that the proposed scheme can maintain the online computational burden at a lower level. The fault detection is performed by a test variable which is formulated by the estimation difference. It is shown that the exact diagnosis of location and time of fault occurrence is achieved within short delay time for any set of threshold values. The proposed scheme can also be applied even multiple simultaneous sensor faults as well as single one. The numerical examples are presented to illustrate the capability of the proposed scheme on a vertical takeoff and landing aircraft system.

A parallel tabu search algorithm for solving the container loading problem

In this paper, a finite impulse response(FIR) fixed-lag smoother is proposed for discrete-time nonlinear systems. If the actual state trajectory is sufficiently close to the nominal state trajectory, the nonlinear system model can be divided into two parts: The error-state model and the nominal model. The error state can be estimated by adapting the optimal time-varying FIR smoother to the error-state model, and the nominal state can be obtained directly from the nominal trajectory model. Moreover, in order to obtain more robust estimates, the linearization errors are considered as a linear function of the estimation errors. Since the proposed estimator has an FIR structure, the proposed smoother can be expected to have better estimation performance than the IIR-structured estimators in terms of robustness and fast convergence. Additionally the proposed method can give a more general solution than the optimal FIR filtering approach, since the optimal FIR smoother is reduced to the optimal FIR filter by setting the fixed-lag size as zero. To illustrate the performance of the proposed method, simulation results are presented by comparing the method with an optimal FIR filtering approach and linearized Kalman filter.

An Evolutionary Parallel Tabu Search approach for distribution systems reinforcement planning

Minimum variance unbiased FIR filter for discrete time-variant systems

Solving the Resource Constrained Project Scheduling Problem using the parallel Tabu Search designed for the CUDA platform

An optimal FIR (finite impulse response) filter and smoother is introduced for the time-varying state-space model. The suggested filter has an FIR structure and utilizes finite observation. It is shown that the impulse response of the optimal FIR filter can be obtained by a simple Riccati-type matrix differential equation. Especially for time-invariant systems, this FIR filter reduces to previously known simple forms. For implementation, a recursive form of the optimal FIR filter and smoother is derived by using adjoint variables, and computational algorithms are suggested. It is also shown by sensitivity analysis that the proposed optimal FIR filter alleviates potential divergence characteristics of the standard Kalman filter.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Solving Multi-criteria Vehicle Routing Problem by Parallel Tabu Search on GPU

In many practical situations, it is necessary to represent the coefficients of a finite impulse response (FIR) digital filter by a finite number of bits. This not only degrades the filter frequency response but also introduces a theoretical limit on the performance of the filter. Derivation of a lower bound on filter degradation is the purpose of this paper. We consider a general case of a length N filter with a discrete set of allowable coefficients. A theorem that gives the lower bound on the increase in minimax approximation error that is caused by the finite wordlength restriction is presented. Its extension and application to filter design cases is demonstrated. The importance of this bound is not only theoretical. Its practical effectiveness is shown in the algorithm for optimal finite wordlength FIR filter design where it significantly reduces the amount of computation.

In this paper, a robust estimation method for estimating the power system harmonics is proposed by using the optimal finite impulse response (FIR) filter. The optimal FIR filter is applied to the state space representation of the noisy current or voltage signal and estimates the magnitude and phase-angle of the harmonic components. Due to the FIR structure, the FIR filter is more robust against model uncertainty than the Kalman filter. Hence, the FIR filter-based method will give a more robust solution for the power system harmonic estimation than the previous Kalman filter-based approaches. The performance and robustness of the proposed method are verified through simulation. Moreover, the proposed method is employed in the power conditioning system to estimate the harmonic components and total harmonic distortions.

In this paper a new multi-objective approach is presented to find the optimal placement of phasor measurement units (PMUs). The proposed method is based on a new parallel tabu search (TS) algorithm, and the lack of communication in substations can be considered, as a constraint. Finally, the solution for different cases are given and discussed.

Performance evaluation of a parallel tabu search task scheduling algorithm

Design and optimisation of regression-type small phase shift FIR filters and FIR-based differentiators with optimal local response in LS-sense