scholarly journals Single-Agent Finite Impulse Response Optimizer for Numerical Optimization Problems

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 9358-9374 ◽  
Author(s):  
Tasiransurini Ab Rahman ◽  
Zuwairie Ibrahim ◽  
Nor Azlina Ab. Aziz ◽  
Shunyi Zhao ◽  
Nor Hidayati Abdul Aziz
Keyword(s):  
Impulse Response ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Finite Impulse Response ◽  
Single Agent

scholarly journals A Study on the Effect of Local Neighbourhood Parameter towards the Performance of SAFIRO

2018 ◽  
Vol 7 (4.27) ◽  
pp. 30 ◽  
Author(s):  
Tasiransurini Ab Rahman ◽  
Zuwairie Ibrahim ◽  
Nor Azlina Ab. Aziz ◽  
Nor Hidayati Abdul Aziz ◽  
Suad Khairi Mohammed ◽  
...  
Keyword(s):  
Impulse Response ◽  
Optimization Problems ◽  
Finite Impulse Response ◽  
Single Agent ◽  
Step Size ◽  
Random Mutation ◽  
Local Neighborhood ◽  
Post Hoc ◽  
Impulse Response Filter ◽  
Neighborhood Method

Single-agent Finite Impulse Response Optimizer (SAFIRO) is a recently proposed metaheuristic optimization algorithm which adopted the procedure of the ultimate unbiased finite impulse response filter (UFIR) in state estimation. In SAFIRO, a random mutation with shrinking local neighborhood method is employed during measurement phase to balance the exploration and the exploitation process. Beta, β, is one of the parameters used in the local neighborhood to control the step size. In this study, the effect of β towards the performance of SAFIRO is observed by assigning the value of 1, 5, 10, 15, and 20. The best setting of β for SAFIRO is also determined. The CEC2014 Benchmark Test Suite is used to evaluate the SAFIRO performance with different β values. Results show that the performance of β is depending on the problems to be optimized. 17 out of 30 functions show the best performance of SAFIRO by setting β = 10. Statistical analysis using Friedman test and Holm post hoc test were performed to rank the performance. β = 10 has the highest rank where its performance is significantly better than other values, but equivalent to β = 5 and β = 15. Hence, it is recommended to tune the β for best performance, however, β = 10 is a good value to be used in SAFIRO for solving optimization problems.  

scholarly journals Multi-Agent Finite Impulse Response Optimizer for Numerical Optimization Problems

2020 ◽  
Author(s):  
Tasiransurini Ab Rahman ◽  
Nor Azlina Ab. Aziz ◽  
Zuwairie Ibrahim ◽  
Nor Hidayati Abdul Aziz ◽  
Mohd Ibrahim Shapiai ◽  
...  
Keyword(s):  
Impulse Response ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Finite Impulse Response ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Metaheuristic Algorithm ◽  
Mathematical Functions ◽  
Random Mutation ◽  
Multi Agent

Abstract This paper investigates the potential of the ultimate iterative unbiased finite impulse response (UFIR) filter as a source of inspiration in a population-based metaheuristic algorithm. Here, a new algorithm inspired by the measurement and estimation procedures of the UFIR filter named the Multi-Agent Finite Impulse Response Optimizer (MAFIRO) for solving numerical optimization problems is proposed. MAFIRO works with a set of agents where each performs the measurement and estimation to find a solution. MAFIRO employs a random mutation of the best-so-far solution and the shrinking local neighborhood method to balance between the exploration and exploitation phases during the optimization process. Subsequently, the performance of MAFIRO is tested by solving the benchmark test suite of the IEEE Congress on Evolutionary Computation 2014. The benchmark is composed of 30 mathematical functions. The competency of MAFIRO is compared with the Particle Swarm Optimization algorithm, Genetic Algorithm, and Grey Wolf Optimizer. The results show that MAFIRO leads in 23 out of 30 functions and has the highest Friedman rank. MAFIRO performs significantly better than the other tested algorithms. Based on the findings, we show that the concept of the UFIR filter is a good inspiration for a population-based metaheuristic algorithm.

scholarly journals A Comparison of Evolutionary Computation Techniques for IIR Model Identification

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Erik Cuevas ◽  
Jorge Gálvez ◽  
Salvador Hinojosa ◽  
Omar Avalos ◽  
Daniel Zaldívar ◽  
...  
Keyword(s):  
Evolutionary Computation ◽  
Impulse Response ◽  
Optimization Problems ◽  
Finite Impulse Response ◽  
Model Identification ◽  
Optimization Techniques ◽  
Infinite Impulse Response ◽  
Computation Techniques ◽  
Recent Developments ◽  
Complex Optimization

System identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR) models for identification is preferred over their equivalent FIR (finite impulse response) models since the former yield more accurate models of physical plants for real world applications. However, IIR structures tend to produce multimodal error surfaces whose cost functions are significantly difficult to minimize. Evolutionary computation techniques (ECT) are used to estimate the solution to complex optimization problems. They are often designed to meet the requirements of particular problems because no single optimization algorithm can solve all problems competitively. Therefore, when new algorithms are proposed, their relative efficacies must be appropriately evaluated. Several comparisons among ECT have been reported in the literature. Nevertheless, they suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. This study presents the comparison of various evolutionary computation optimization techniques applied to IIR model identification. Results over several models are presented and statistically validated.

scholarly journals Design of Finite Impulse Response Filters Based on Genetic Algorithm

2013 ◽  
Vol 6 (3) ◽  
pp. 28-39
Author(s):  
Raaed Faleh Hassan ◽  
Ali Subhi Abbood
Keyword(s):  
Genetic Algorithm ◽  
Impulse Response ◽  
Optimization Problems ◽  
Filter Design ◽  
Finite Impulse Response ◽  
Optimal Solution ◽  
Fir Filter ◽  
Language Version ◽  
Finite Impulse Response Filters ◽  
Matlab Programming

Genetic Algorithms (GAs) are used to solve many optimization problems in science and engineering such as pattern recognition, robotics, biology, medicine, and many other applications. The aim of this paper is to describe a method of designing Finite Impulse Response (FIR) filter using Genetic Algorithm (GA). In this paper, the Genetic Algorithm not only used for searching the optimal coefficients, but also it is used to find the minimum number of Taps, and hence minimize the number of multipliers and adders that can be used in the design of the FIR filter. The Evolutionary Programming is the best search procedure and most powerful than Linear Programming in providing the optimal solution that is desired to minimize the ripple content in both passband and stopband. The algorithm generates a population of genomes that represents the filter coefficient and the number of taps, where new genomes are generated by crossover and mutation operations methods. Our proposed genetic technique has able to give better result compare to other method.The FIR filter design using Genetic Algorithm is simulated using MATLAB programming language version 7.6.0.324 (R2008a).

scholarly journals Single-agent Finite Impulse Response Optimizer vs Simulated Kalman Filter Optimizer

Mekatronika ◽  
2019 ◽  
Vol 1 (2) ◽  
pp. 15-22
Author(s):  
Tasiransurini Ab Rahman ◽  
Nor Azlina Ab. Aziz ◽  
Nor Hidayati Abdul Aziz
Keyword(s):  
Kalman Filter ◽  
Impulse Response ◽  
Optimization Algorithm ◽  
Applied Research ◽  
Finite Impulse Response ◽  
Single Agent ◽  
Optimal Solution ◽  
Optimal Estimation ◽  
Performance Comparison ◽  
Repetition Number

Single-agent Finite Impulse Response Optimizer (SAFIRO) is a new estimation-based optimization algorithm which mimics the work procedure of the ultimate unbiased finite impulse response (UFIR) filter. In a real UFIR filter, the horizon length, N, plays an important role to obtain the optimal estimation. In SAFIRO, N represents the repetition number of estimation part that needs to be done in find-ing an optimal solution. On the other hand, Simulated Kalman Filter (SKF) is also an estimation- based optimization algorithm inspired by the estimation capability of Kalman filtering. In literature, substantial amount of works has been devoted to SKF, both in applied research and fundamental enhancements. Thus, in this paper, a performance comparison of both SAFIRO and SKF is presented. It is found that the SAFIRO outperforms the SKF significantly.

Evaluation of Different Horizon Lengths in Single-agent Finite Impulse Response Optimizer

2019 ◽  
Author(s):  
Tasiransurini Ab Rahman ◽  
Zuwairie Ibrahim ◽  
Nor Hidayati Abdul Aziz ◽  
Nor Azlina Ab. Aziz ◽  
Mohd Saberi Mohamad ◽  
...  
Keyword(s):  
Impulse Response ◽  
Finite Impulse Response ◽  
Single Agent

scholarly journals NANO-studio, the design environment of filter banks implemented in standard CMOS technology

2021 ◽  
Author(s):  
Andrzej Handkiewicz ◽  
Mariusz Naumowicz
Keyword(s):  
Impulse Response ◽  
Filter Banks ◽  
Finite Impulse Response ◽  
Infinite Impulse Response ◽  
Cmos Process ◽  
Digital Cmos ◽  
Additional Advantage ◽  
Analysis And Synthesis ◽  
Optimization Parameters ◽  
Synthesis Filter

AbstractThe paper presents a method of optimizing frequency characteristics of filter banks in terms of their implementation in digital CMOS technologies in nanoscale. Usability of such filters is demonstrated by frequency-interleaved (FI) analog-to-digital converters (ADC). An analysis filter present in these converters was designed in switched-current technique. However, due to huge technological pitch of standard digital CMOS process in nanoscale, its characteristics substantially deviate from the required ones. NANO-studio environment presented in the paper allows adjustment, with transistor channel sizes as optimization parameters. The same environment is used at designing a digital synthesis filter, whereas optimization parameters are input and output conductances, gyration transconductances and capacitances of a prototype circuit. Transition between analog s and digital z domains is done by means of bilinear transformation. Assuming a lossless gyrator-capacitor (gC) multiport network as a prototype circuit, both for analysis and synthesis filter banks in FI ADC, is an implementation of the strategy to design filters with low sensitivity to parameter changes. An additional advantage is designing the synthesis filter as stable infinite impulse response (IIR) instead of commonly used finite impulse response (FIR) filters. It provides several dozen-fold saving in the number of applied multipliers.. The analysis and synthesis filters in FI ADC are implemented as filter pairs. An additional example of three-filter bank demonstrates versatility of NANO-studio software.

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