Multiobjective Optimization of an Operational Amplifier by the Ant Colony Optimisation Algorithm

Benhala, Bachir; Ahaitouf, Ali; Mechaqrane, Abdellah

doi:10.5923/j.eee.20120204.09

Cited by 21 publications

(8 citation statements)

References 26 publications

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“…Therefore, many optimization schemes for analog circuit design have been reported by many researchers. The most well-known techniques are g m /I D methodology [36,37], mathematical heuristics such as Particle Swarm Optimization (PSO) [38], Ant Colony Optimization (ACO) [39], Genetic Algorithms (GA) [40], and Symbiotic Organisms Search Algorithm [41]. The g m / I D methodology is a popular one used for sizing low power and low noise analog building blocks using a simulationbased datasheet of transistors.…”

Section: Noise Analysismentioning

confidence: 99%

An ultra-low-power near rail-to-rail pseudo-differential subthreshold gate-driven OTA with improved small and large signal performances

Ghosh

Bhadauria

2021

Analog Integr Circ Sig Process

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An efficient architecture of a high-performance ultra-low-voltage gate-driven two-stage pseudo-fully differential operational transconductance amplifier (OTA) is presented. The proposed subthreshold-region operated OTA utilizes two identical conventional current-mirror-based source coupled pseudo-differential amplifiers with their cross-connected gate as core amplifier blocks. The input core differential pair exploiting forward body terminal biasing scheme at output load employed in cascaded stages essentially improves the overall transconductance more than twice compared to the conventional pseudo-differential pair based OTA and achieves near rail-to-rail output voltage swing (90.2% of supply voltage) for near rail-to-rail input common-mode voltage (94.92% of supply voltage). The circuit is designed and optimized using g m /I D technique associated with the Particle Swarm Optimization (PSO) algorithm with a single supply of 0.35 V for a capacitive load of 10 pF and simulated in Cadence Virtuoso analog environment using UMC 180-nm CMOS technology. Post-layout simulations have been accomplished to justify the performance of the proposed OTA. It achieves an open-loop gain of 83.00 dB, phase margin of 61.48°, and power dissipation of 35.04 nW at a unity gain frequency of 24.78 kHz. A low-frequency continuous second-order G m -C filter is also implemented to validate its workability. Simulation results of this work are compared to the state-of-the-art architecture available, and enhanced performance is validated. Keywords Operational transconductance amplifier (OTA) Á Unity gain frequency (UGF) Á Subthreshold region Á Input common-mode range (ICMR) Á Biquad G m -C filter & Sougata Ghosh

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Section: Noise Analysismentioning

confidence: 99%

An ultra-low-power near rail-to-rail pseudo-differential subthreshold gate-driven OTA with improved small and large signal performances

Ghosh

Bhadauria

2021

Analog Integr Circ Sig Process

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“…The fitness function is often formulated as cost minimization of the solution of the given problem. The ACO algorithm has received much attention and has been incorporated in a number of optimization problems [16][17][18][19][20][21][30][31][32][33]43].…”

Section: The Aco Algorithm For Synthesis Of Mvl Functionsmentioning

confidence: 99%

Swarm intelligence versus direct cover algorithms in synthesis of Multi-Valued Logic functions

Abd-El-Barr

Qureshi

Sarif

2020

ACI

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Ant Colony Optimization and Particle Swarm Optimization represent two widely used Swarm Intelligence (SI) optimization techniques. Information processing using Multiple-Valued Logic (MVL) is carried out using more than two discrete logic levels. In this paper, we compare two the SI-based algorithms in synthesizing MVL functions. A benchmark consisting of 50,000 randomly generated 2-variable 4-valued functions is used for assessing the performance of the algorithms using the benchmark. Simulation results show that the PSO outperforms the ACO technique in terms of the average number of product terms (PTs) needed. We also compare the results obtained using both ACO-MVL and PSO-MVL with those obtained using Espresso-MV logic minimizer. It is shown that on average, both of the SI-based techniques produced better results compared to those produced by Espresso-MV. We show that the SI-based techniques outperform the conventional direct-cover (DC) techniques in terms of the average number of product terms required.

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“…As per the fig. 1(a,b,c) below when Ant opts the final route, then this optimization in itself causes the congestion in real-world traffic scenario [19].…”

Section: Proposed Effiecent Routing Algorithm For Platooningmentioning

confidence: 99%

Swarm Intelligence based efficient routing algorithm for platooning in VANET through Ant Colony Optimization

Singh¹,

Prateek²,

Sastry³

2019

IJITEE

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Today’s era is of smart technology, Computing intelligence and simulations. Many areas are now fully depended on simulation results for implementing real time workflow. Worldwide researchers and many automobile consortium are working to make intelligent Vehicular Ad hoc Network but till yet it is just a theory-based permutation. If we take VANET routing procedures then it is mainly focussing on AODV, DSDV and DSR routing protocols. Similarly, one more area of Swarm Intelligence is also attained attention of industry and researchers. Due the behavior of dynamic movement of vehicle and ants, Ant Colony Optimization is best suited for VANET performance simulations. Much of the work has already done and in progress for routing protocols in VANET but not focused on platooning techniques of vehicle nodes in VANET. In our research idea, we came up with a hypothesis that proposes efficient routing algorithm that made platooning in VANET optimized by minimizing the average delay waiting and stoppage time. In our methodology, we have used OMNET++, SUMO, Veins and Traci for testing of our hypothesis. Parameters that we took into consideration are end-to-end delay as an average, packet data delivery ratio, throughput, data packet size, number of vehicle nodes etc. Swarm Intelligence has proved a way forward in VANET scenarios and simulation for more accurate results. In this paper, we implemented Ant Colony Optimization technique in VANET simulation and proved through results that if it integrates with VANET routing scenarios then result will be at its best.

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Multiobjective Optimization of an Operational Amplifier by the Ant Colony Optimisation Algorithm

Cited by 21 publications

References 26 publications

An ultra-low-power near rail-to-rail pseudo-differential subthreshold gate-driven OTA with improved small and large signal performances

An ultra-low-power near rail-to-rail pseudo-differential subthreshold gate-driven OTA with improved small and large signal performances

Swarm intelligence versus direct cover algorithms in synthesis of Multi-Valued Logic functions

Swarm Intelligence based efficient routing algorithm for platooning in VANET through Ant Colony Optimization

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