A novel self-adaptive differential evolution algorithm for efficient design of multiplier-less low-pass FIR filter

The everyday broadening field of signal processing has digital filters to play a major role. Linear phase FIR filters are used in vast number of applications due to their nature of phase linearity as well as frequency stability. The traditional nonoptimization methods available for filter design suffer from the problem of need for analog to digital conversion and also the inefficient control of frequency response. The conventional gradient based optimization methods are unable to solve non-differential functions and converges to local optimum solution. Thus this paper presents the evolutionary optimization technique of Particle Swarm Optimization (PSO) for the design of linear phase digital low pass (LP) FIR filter. Given the specifications of desired filter to be realized, PSO algorithm results in an optimal coefficient set for linear phase FIR filter approximating the ideal specifications. In this paper PSO algorithm is used with constriction factor approach to solve the multimodal, highly non-linear filter design problem. This method has the property of parameter independence and thus ensuring convergence while fully exploring the solution space. The velocity and position updating rules of original PSO algorithm is used for the design of low pass FIR filter of order 20. The extensive simulation results obtained from the proposed method shows superiority of the algorithm.

Differential Evolution (DE) is possibly the most current powerful stochastic real-parameter optimization algorithm and has been used in multiple diverse area such as neural networks, logistics, scheduling, modelling and others. Its simplicity, ease of implementation and reliability had captures many practitioners and scientists in implementing the algorithm. As different problems require different parameter setting, the implementation of DE in tackling complex computational optimization problem is quite challenging. Nevertheless, success of the algorithm depends on the ability to choose the right parameter setting based on problems in hand. Thus, extra attention is needed in order to fine tune the perfect parameter for each problem. Self-adaptive Differential Evolution (SADE) algorithm had been introduced in order to simplify the search for the right parameter to be used in DE algorithm. With the introduction of SADE in optimization areas, where the choice of learning strategy and parameter setting do not require predefining, parameter tuning has become less confusing. This paper aims at providing an overview on significant application that have benefited from SADE implementation. SADE had been applied in numerous disciplines such as electromagnetics, power system, computer performance, fermentation, polyester process and more. SADE has also proven to achieve better performance compared to conventional DE algorithm. By collecting and analyzing related articles that have implemented SADE in solving problem, a significant trends on the application of SADE will be provided.

In the design of multiplierless FIR filters, researchers have made every effort to reduce the number of adders when coefficients multipliers are realized using adder-and-shift network to decrease the overall chip area. However, with the advance of IC technology, area becomes a less important issue than the speed. In this paper, we propose a speed oriented optimization of linear phase FIR filters, where the length of critical path is used as the criteria in the discrete coefficient search. The length of critical path is measured as the number of cascaded full adders rather than the traditional adder depth. Compared to the area oriented algorithm, the proposed algorithm can generate the filters with much shorter critical path delay and meanwhile the area-delay product is also reduced. Gate level simulations of benchmark filters verify the above claim.

In this communication, one novel attempt has been made towards the design of minimum-phase finite impulse response (FIR) filter using one evolutionary computation technique, named differential evolution (DE) algorithm. Mean square error (MSE) between the frequency samples of the ideal filter and the designed filter in the pass-band and stop-band region and the minimum average error (MAE) between the frequency samples in the transition-band region had been considered for the formulation of the objective function in the optimization process. Performance of the optimization technique has been monitored in terms of convergence characteristics for four different orders of FIR filter under consideration. Robustness of our proposition has been firmly established in the sense that the proposed algorithm can be suitably tuned for the design of low-pass, high-pass or band-pass FIR filter of different specifications. Supremacy of the proposed algorithm over other existing techniques for the design of minimum-phase FIR filter has been substantiated by means of frequency response and pole-zero diagrams.

This paper describes the design of variable FIR filters which have magnitude and group delay flatness at /spl omega/ = 0, and have zeros at /spl omega/ = /spl pi/. The filter coefficients are given by multidimensional polynomials of variable parameters to specify variable frequency characteristics, and optimal polynomial coefficients are obtained by minimizing squared error function subject to flat magnitude and group delay constraints. The flatness conditions are transformed so as to be satisfied irrespective of variable parameters. Because the derived conditions are given as simple equality constraints, the optimal polynomial coefficients can be easily obtained. A numerical example is given to show the validity of the proposed technique.

Differential evolution (DE), as a very powerful population-based stochastic optimizer, is one of the most active research topics in the field of evolutionary computation. Self-adaptive differential evolution (SaDE) is a well-known DE variant, which aims to relieve the practical difficulty faced by DE in selecting among many candidates the most effective search strategy and its associated parameters. SaDE operates with multiple candidate strategies and gradually adapts the employed strategy and its accompanying parameter setting via learning the preceding behavior of already applied strategies and their associated parameter settings. Although highly effective, SaDE concentrates more on exploration than exploitation. To enhance SaDE's exploitation capability while maintaining its exploration power, we incorporate local search chains into SaDE following two different paradigms (Lamarckian and Baldwinian) that differ in the ways of utilizing local search results in SaDE. Our experiments are conducted on the CEC-2014 real-parameter single-objective optimization testbed. The statistical comparison results demonstrate that SaDE with Baldwinian local search chains, armed with suitable parameter settings, can significantly outperform original SaDE as well as classic DE at any tested problem dimensionality.

A novel trajectory optimization technique has been proposed in this paper to generate reference trajectory for Satellite Launch Vehicle (SLV). For this purpose, a suitable objective function has been formulated subsuming parameters like height, velocity, injection angle and payload mass. The contribution of these parameters in evolving the objective function has been represented by assigning different weighting factors against the different parameters. Moreover, optimal selection of the setting of parameters has also been achieved by proper optimization of the weighting factors associated with the objective function. In the next step, an attempt has been made to optimize the objective function using the existing Differential Evolution (DE) scheme. As an enhancement of this work, a novel Self Adaptive Differential Evolution (SADE) algorithm has been proposed to optimize the trajectory in a more effective manner. Finally, a comparative study has been presented between the proposed SADE based design and the existing DE based design.

Evolutionary computational techniques have been employed judiciously in various signal processing applications of late. In this paper, such an attempt has been made to design a low-pass linear-phase multiplier-less finite duration impulse response (FIR) filter using differential evolution (DE) algorithm. This particular evolutionary optimization technique has been explored to search the impulse response coefficients of the FIR filter in the form of sum of power of two (SPT) in order to avoid the multipliers during design process. The performance of the designed low-pass filter has been studied thoroughly in terms of its frequency characteristics and primitive requirement of fundamental hardware blocks. The superiority of our design has been ascertained over a number of existing techniques by various means. Finally, the proposed filter of different lengths has been implemented on a field programmable gate array (FPGA) chip for evaluating the competency of this work. The percentage improvement in hardware complexity produced by our design has also been computed and clearly listed in this paper for convenience.

This paper considers the design of reduced complexity two-dimensional FIR filters using genetic algorithms (GAs). Circularly symmetric and diamond shaped low-pass linear phase FIR filters are designed using coefficients comprising the sum or difference of two power-of-two terms. A minimax error criterion is adopted which leads to a minimisation of the weighted ripple extrema in both pass and stop bands. The results presented are compared with those obtained using simulated annealing, linear programming and simple rounding of an optimum (continuous) minimax solution. The filters designed using GAs are found to be as good or better than those designed using other methods, with less computational effort than the simulated annealing approach.

We present a technique for the design of multiplierless FIR filters with canonic signed digit (CSD) coefficients based on higher order sigma-delta modulation with a CSD quantizer (/spl Sigma//spl Delta/-CSD). The CSD quantizer yields FIR filter coefficients quantized to sums of a few signed powers-of-two digits, eliminating the need for multiplication. The sigma-delta modulation moves the quantization error out of the band of interest into a frequency band where the increased error can be attenuated by a cascaded prefilter in a prefilter-equalizer structure, or be attenuated by a decimation filter in a multi-rate approach. The use of higher order sigma-delta modulation provides more flexibility in moving the quantization error than the previously proposed first-order modulation. We give lowpass and bandpass examples where the filters designed by the higher order modulation demonstrate substantial frequency characteristic improvement. An empirical design guideline to ensure the stability for the higher order /spl Sigma//spl Delta/-CSD modulation is also given in the paper.

Reduction of structural complexity of digital systems has been emerging as one of the major areas of concern to the system designers. The deployment of any physical circuit or system in any specific application is solely determined by the intricacy of the concerned circuit which restricts the use of sophisticated circuits in high speed operation. Thus the design of computationally efficient structure has drawn special attention to the modern researchers. Due to the immense development of evolutionary computation techniques over the last few years, they are being increasingly used for a number of signal processing applications. This paper deals with one such robust technique called Differential Evolution (DE) and the impact of its different mutation strategies for efficient design of multiplier-less low-pass Finite duration Impulse Response (FIR) filter. In order to find out the most favourable mutation scheme, the computational efficiency of various mutation schemes has been studied. For this purpose, the convergence behaviour and error histogram of DE algorithm has been presented. From the simulation results, the name of the computationally most efficient mutation strategy has been suggested. Finally, the superiority of the established mutation strategy has been reconfirmed in terms of the filter performance.

SummaryModern power systems are being shifted its attention towards distributed generations (DGs). DGs have allured more concern as an auspicious opportunity with considerable economic profits. To minimize the power generation costs of DGs, day‐to‐day operation scheduling is essential. The role of this study is to offer an optimal operation schedule for DG with several energy sources including renewable energy sources (RES), considering economic facets. In order to achieve the cost minimization along with optimal scheduling, an objection function has been formulated and solved using the optimization algorithms. This study aims to present the applications of differential evolution (DE) algorithm and its variants such as opposition‐based differential evolution (ODE), self‐adaptive differential evolution (SaDE), improved differential evolution (IDE), and cultivated differential evolution (CuDE) for scheduling DG optimally. A scheme for optimal scheduling of thermal, wind power, and solar PV generators has been evaluated. The simulations have been carried out on IEEE 14 bus system, IEEE 30 bus system, IEEE 57 bus system, and Tamil Nadu Generation and Distribution Corporation Limited (TANGEDCO), as a real part of 62 bus Indian utility system (IUS). The novelty of this study lies in simulating a real‐time system for solving optimal scheduling problem, in that way helping decision makers to choose the optimal operation points. The results indicate that the SaDE outperformed other DE variants by giving the best fitness value and convergence rate.

Insights into the effects of control parameters and mutation strategy on self-adaptive ensemble-based differential evolution

One ideal performance of this design is in the areas of decimation where a decimation factor of 10, 45-order and pass band ripple of 1dB and interpolation of sampled rates where a sinusoidal signal input produced a ripple free output with interpolation factor of 10, 52-order and stopband attenuation of 60dB. Owing to the multiple samples of filter length of 200, the filter performed down sampling preceded with filtering as well as up sampling preceded with filtering, hence multi-rate filter by allowing a low threshold of frequency of to be passed, blocking a high threshold of and vice versa. There was resampled output increased to 150% preceded by filtering. The filter coefficients for low pass and high pass Digital FIR filter, through the least square regression method, parks McClellan Algorithm and window methods were employed for easy optimization. More so, there was creation of 2-4-5 filter channel banks through the 2nd-level convolution of their down sampling and up sampling filtering techniques during the multi-irate filtering to ensure the design of error-free Digital FIR Filter using MATLAB File editor(M-File) and tool boxes for writing the C-programming of the design. In the analysis, the mean and standard deviation of the low pass Digital FIR Filter output during decimation and interpolation are (0.26, 6.13) and (0.004,1.22) respectively.

Digital filter plays an important role in the advanced era of communication system. Digital filters basically provide high attenuation to the unwanted ones and offer very low or ideally zero attenuation to desired signal components when its impulse response is adjusted as per requirement. But the design of a digital filter satisfying all the required conditions is a challenging task. In this paper, design techniques of low pass FIR filters using Tukey window, Kaiser Window, and Gaussian window methods are presented. The magnitude and phase responses are demonstrated in the simulation section for the different design techniques of digital low pass FIR filters. From the simulation results, it is observed that for a longer length the FIR filter has a sharper cut-off FIR filter and a narrow transition band.