Hybrid heuristic search method for design of digital IIR filter with conflicting objectives

Sidhu, Darshan Singh; Dhillon, J.S.; Kaur, Dalveer

doi:10.1007/s00500-015-2023-8

Cited by 12 publications

(3 citation statements)

References 22 publications

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“…However, time complexities were not compared. For the same problem (Sidhu et al, 2016) introduced a hybrid search procedure involving DE for global search and evolutionary search algorithm using binary successive approximation for local search.…”

Section: Iir System Identification and Filter Designmentioning

confidence: 99%

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Gotmare

Bhattacharjee

Patidar

et al. 2017

Swarm and Evolutionary Computation

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Section: Iir System Identification and Filter Designmentioning

confidence: 99%

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Gotmare

Bhattacharjee

Patidar

et al. 2017

Swarm and Evolutionary Computation

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“…Since DE was introduced, numerous studies have been conducted to design new DE variants in an effort to improve performance, including adaptive trial vector generation strategy schemes [31,32,33,34,35,36], adaptive and self-adaptive parameter control schemes [37,38,39,40,41,42,43,44], ensemble DE variants [45,46,47], and incorporating external techniques such as α-stable distribution based mutations [48,49,50,51], neighborhood-based mutations [52], and OBLs [53,54,55,56,57,58,59,60]. For more detailed explanations of the state-of-the-art DE variants, please refer to the following surveys [5,3,6,7].…”

Section: Advanced Differential Evolution Variantsmentioning

confidence: 99%

A Fast and Efficient Stochastic Opposition-Based Learning for Differential Evolution in Numerical Optimization

Choi¹,

Togelius²,

Cheong³

2019

Preprint

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A new variant of stochastic opposition-based learning (OBL) is proposed in this paper. OBL is a relatively new machine learning concept, which consists of simultaneously calculating an original solution and its opposite to accelerate the convergence of soft computing algorithms. Recently a new opposition-based differential evolution (ODE) variant called BetaCODE was proposed as a combination of differential evolution and a new stochastic OBL variant called BetaCOBL. BetaCOBL is capable of flexibly adjusting the probability density functions used to calculate opposite solutions, generating more diverse opposite solutions, and preventing the waste of fitness evaluations. While it has shown outstanding performance compared to several state-of-the-art OBL variants, BetaCOBL is challenging with more complex problems because of its high computational cost. Besides, as it assumes that the decision variables are independent, there is a limitation in the search for decent opposite solutions on inseparable problems. In this paper, we propose an improved stochastic OBL variant that mitigates all the limitations of BetaCOBL. The proposed algorithm called iBetaCOBL reduces the computational cost from O(NP 2 •D) to O(NP•D) (NP and D stand for population size and dimension, respectively) using a linear time diversity measure. In addition, iBetaCOBL preserves the strongly dependent decision variables that are adjacent to each other using the multiple exponential crossover. The results of the performance evaluations on a set of 58 test functions show that iBetaCODE finds more accurate solutions than ten state-of-the-art ODE variants including BetaCODE. Additionally, we applied iBetaCOBL to two state-of-the-art DE variants, and as in the previous results, iBetaCOBL based variants exhibit significantly improved performance.

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“…It can achieve accurate frequency selectivity with a lower order. Over the years, many scholars have done a lot of explorations on IIR filter design [11][12][13][14] . Usually designing IIR digital filter need to design the analog filter firstly, and then transform it into a digital filter.…”

Section: Introductionmentioning

confidence: 99%

The Design Of Intelligent Filter For EEG

Dachao

Liu

2018

MATEC Web Conf.

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EEG data processing method is usually digital filter designed by the traditional method. Its disadvantage is the transition zone is wide and the filtering effect is poor. Using an improved particle swarm optimization algorithm on IIR digital filters design, the performances of filters designed by various methods are compared and analyzed. Experiments illustrate particle swarm optimization algorithm is effective in IIR filter design and its performance is promising.

show abstract

Hybrid heuristic search method for design of digital IIR filter with conflicting objectives

Cited by 12 publications

References 22 publications

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

Swarm and evolutionary computing algorithms for system identification and filter design: A comprehensive review

A Fast and Efficient Stochastic Opposition-Based Learning for Differential Evolution in Numerical Optimization

The Design Of Intelligent Filter For EEG

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