Low power FIR filter design using modified multi-objective artificial bee colony algorithm

Dwivedi, Atul Kumar; Ghosh, Subhojit; Londhe, Narendra D.

doi:10.1016/j.engappai.2016.06.006

Cited by 40 publications

(20 citation statements)

References 55 publications

(66 reference statements)

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“…The hardware utilization is more (slice and four input slices) in FIR-ABC algorithm [21]. To [17] and FIR-ABC algorithm [21]. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…In PSA-FIR [17], the Wallace Tree Adder (WTA) has an irregular structure that requires more execution time. The hardware utilization is more (slice and four input slices) in FIR-ABC algorithm [21]. To [17] and FIR-ABC algorithm [21].…”

Section: Resultsmentioning

confidence: 99%

“…[17] FIR-ABC Algorithm[21] LA-CSLA-FIRInternational Journal of Intelligent Engineering and Systems, Vol.12, No.4, 2019 DOI: 10.22266/ijies2019.0831.10…”

mentioning

confidence: 99%

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Low Area VLSI implementation of CSLA for FIR Filter Design

Kumar¹,

Gangappa²

2019

IJIES

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Finite Impulse Response (FIR) filter is a major building block in Digital Signal Processing (DSP) system. The conventional FIR filters are implemented by utilizing a normal adder, which consumes more area. Minimizing the hardware utilization of the adder is a significant challenge in FIR filter design. Generally, complexity of the FIR filter is dominated by the adder. So, an efficient FIR filter was designed with the help of Carry Select Adder (CSLA) to reduce area and hardware complexity of the accumulation block. The area has been reduced by using CSLA that is very significant idea while designing a less area filter. The proposed method named as Low Area-CSLA-FIR (LA-CSLA-FIR) filter. The LA-CSLA-FIR filter was implemented in Xilinx Field Programmable Gate Array (FPGA) on different devices such as Virtex-4, Virtex-5 and Virtex-6 by using Verilog code. The experimental results of the LA-CSLA-FIR filter design reduced average the FPGA device utilization: 15.38 % of LUTs, 8% of flip flops and 8% of slices on Virtex-5 compared to existing filter designs.

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“…The hardware utilization is more (slice and four input slices) in FIR-ABC algorithm [21]. To [17] and FIR-ABC algorithm [21]. Fig.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Low Area VLSI implementation of CSLA for FIR Filter Design

Kumar¹,

Gangappa²

2019

IJIES

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“…Recently, nature-inspired evolutionary algorithms are the interesting approach that have mostly investigated due to the powerful performance and efficiently in solving any types of complex problems. Several algorithms such as the firefly algorithm [19] and the artificial bee colony [20] are among of the approaches that have been widely presented. Similarly, the particle swarm optimization (PSO) is among them that able to give a better performance, very efficient and easy to be used.…”

Section: Introductionmentioning

confidence: 99%

Effect of Fuzzy Logic Based-Skyhook Policy with Particle Swarm Optimization for Semi-Active Ride Comfort

Talib¹,

Darus²,

Samin³

2018

IJET

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This paper presents the effect of the fuzzy logic based-skyhook policy tuned using particle swarm optimization (FLSP-PSO) for semi-active ride comfort of quarter vehicle model. Spencer model was used to represent the magnetorheological damper model and its behavior was investigated in the form of force-displacement and force-velocity characteristics. The fuzzy logic control adopted with the skyhook policy based on Sugeno-type fuzzy was used to enhance the ride performance. An intelligent evolutionary algorithm known as the particle swarm optimization was also adapted in the proposed controller to compute the fuzzy gain scaling. The performance of the FLSP-PSO controller is compared to other controller responses. The effect of the PSO techniques to optimize the FLPS parameters gives a better performance and able to improve the vehicle ride comfort than its counterparts.

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“…EAs mimic various social behaviors existing in nature to solve the optimization problems. Some popular EAs include genetic algorithm (GA) [1,2], particle swarm optimization (PSO) [3,4], artificial immune system (AIS) [5], differentiable evolution (DE) [6,7], ant colony optimization (ACO) [8], artificial bee colony (ABC) [9,10], and simulated annealing algorithm (SA) [11].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Performance of Biogeography‐Based Optimization Using Multitopology and Quantitative Orthogonal Learning

Wen

Chen

et al. 2017

Mathematical Problems in Engineering

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Two defects of biogeography-based optimization (BBO) are found out by analyzing the characteristics of its dominant migration operator. One is that, due to global topology and direct-copying migration strategy, information in several good-quality habitats tends to be copied to the whole habitats rapidly, which would lead to premature convergence. The other is that the generated solutions by migration process are distributed only in some specific regions so that many other areas where competitive solutions may exist cannot be investigated. To remedy the former, a new migration operator precisely developed by modifying topology and copy mode is introduced to BBO. Additionally, diversity mechanism is proposed. To remedy the latter defect, quantitative orthogonal learning process accomplished based on space quantizing and orthogonal design is proposed. It aims to investigate the feasible region thoroughly so that more competitive solutions can be obtained. The effectiveness of the proposed approaches is verified on a set of benchmark functions with diverse characteristics. The experimental results reveal that the proposed method has merits regarding solution quality, convergence performance, and so on, compared with basic BBO, five BBO variant algorithms, seven orthogonal learning-based algorithms, and other non-OL-based evolutionary algorithms. The effects of each improved component are also analyzed.

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Low power FIR filter design using modified multi-objective artificial bee colony algorithm

Cited by 40 publications

References 55 publications

Low Area VLSI implementation of CSLA for FIR Filter Design

Low Area VLSI implementation of CSLA for FIR Filter Design

Effect of Fuzzy Logic Based-Skyhook Policy with Particle Swarm Optimization for Semi-Active Ride Comfort

Enhancing the Performance of Biogeography‐Based Optimization Using Multitopology and Quantitative Orthogonal Learning

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