Ant Colony Optimization for Optimal Low-Pass Filter Sizing

Loubna, Kritele; Benhala, Bachir; Zorkani, Izeddine

doi:10.1007/978-3-319-95104-1_18

Cited by 14 publications

(12 citation statements)

References 16 publications

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“…Some Meta-heuristics are used by the analog designers to solve the design problems of integrated circuits and also of discrete component systems, such as Simulated Annealing (SA) [8], Genetic Algorithms (GA) [9], Tabu Search (TS) [10], Particle Swarm Optimization (PSO) [11], Ant Colony Optimization (ACO) [12][13][14] and Artificial Bee Colony (ABC) [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A Genetic algorithm for the optimal design of a multistage amplifier

Beqal

Benhala

Zorkani

2020

IJECE

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The optimal sizing of analog circuits is one of the most complicated processes, because of the number of variables taken into, to the number of required objectives to be optimized and to the constraint functions restrictions. The aim is to automate this activity in order to accelerate the circuits design and sizing. In this paper, we deal with the optimization of the three stage bipolar transistor amplifier performances namely the voltage gain (AV), the input impedance (ZIN), the output impedance (ZOUT), the power consumption (P) and the low and the high cutoff frequency (FL,FH), through the Genetic Algorithm (GA). The presented optimization problem is of multi-dimensional parameters, and the trade-off of all parameters. In fact, the passive components (Resistors and Capacitors) are selected from manufactured constant values (E12, E24, E48, E96, E192) for the purpose of reduce the cost of design; also, the intrinsic parameters of transistors (hybrid parameters and the junction capacitances) are considered variables in order not to be limited in design. SPICE simulation is used to validate the obtained result/performances.

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Section: Introductionmentioning

confidence: 99%

A Genetic algorithm for the optimal design of a multistage amplifier

Beqal

Benhala

Zorkani

2020

IJECE

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“…In this field, the methods mostly used are EA: 'Evolutionary Algorithms ' [7], such as the Differential Evolution (DE) Algorithm [8], and the Genetic Algorithm (GE) [9], [10], but in the last two decades, a new group of nature-inspired heuristic optimization algorithms have been introduced as SI: 'Swarm Intelligence Techniques', such as Ant Colony Optimization (ACO) [11], [12], Gravitational Search Algorithm (GSA) [13], Artificial Bee Colony (ABC) [14], Dragonfly Algorithm (DA) [15], Particle Swarm Optimization (PSO) [16], Grey Wolf Optimizer (GWO) [17], and Bacterial Foraging Optimization (BFO) [18].…”

Section: Introductionmentioning

confidence: 99%

Shape Optimization of Planar Inductors for RF Circuits using a Metaheuristic Technique based on Evolutionary Approach

Hajjami¹,

Benhala²,

Bouyghf³

2020

Adv. sci. technol. eng. syst. j.

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In this article, we concentrate on the use of a metaheuristic technique based on an Evolutionary Algorithm (EA) for determining the optimal geometrical parameters of spiral inductors for RF circuits. For this purpose, we have opted for an optimization procedure through an enhanced Differential Evolution (DE) algorithm. The proposed tool allows the design of optimized integrated inductors not only with a maximum quality factor(Q), but also with a maximum self-resonant frequency (SRF), and a minimum surface area, in addition to being adapted to any model of any technology. This paper presents also a comparison between performances of the optimized inductors (inductor square shape and inductor circular shape), in terms of the quality factor, SRF, and circuit size. For the purpose of mitigating the impact of parasitic effects, design basics have been taken into consideration. Then, in order to investigate the efficacy of evaluated results, an (EM) simulator has been employed.

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“…They are efficient, resourceful, and called swarm intelligence techniques (SI) [12]. SI techniques concentrate on animal and insects conduct for developing some meta-heuristics, namely particle swarm optimization (PSO) [13], artificial bee colony (ABC) [14,15] and ant colony optimization (ACO) [16,17]. We concentrate in this article on the use of three meta-heuristic techniques namely Artificial Bee Colony, Ant Colony Optimization and Differential Evolution for the optimal sizing of RF integrated square spiral inductors.…”

Section: Introductionmentioning

confidence: 99%

An optimal design of square spiral integrated inductor using metaheuristic techniques

Abi

Bouyghf

Benhala

et al. 2020

IJEECS

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In this paper, the optimal sizing of CMOS RF square spiral integrated inductor utilizing three meta-heuristic techniques namely Ant Colony Optimization, Artificial Bee Colony and Differential Evolution is presented. The π-model is employed for the characterization of inductor behavior. In this optimization procedure, the geometrical parameters of the CMOS RF square spiral integrated inductor are considered as the design variables that satisfy the most important constraints such as the fixed value of required inductance 4nH at the operating frequency 2.4 GHz. The design of the integrated square spiral inductor is done with UMC 130 nm CMOS technology. A comparison between the used meta-heuristic techniques is emphasized. The optimization results are checked and validated by the mean of the Momentum Advanced Design System (ADS).

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Ant Colony Optimization for Optimal Low-Pass Filter Sizing

Cited by 14 publications

References 16 publications

A Genetic algorithm for the optimal design of a multistage amplifier

A Genetic algorithm for the optimal design of a multistage amplifier

Shape Optimization of Planar Inductors for RF Circuits using a Metaheuristic Technique based on Evolutionary Approach

An optimal design of square spiral integrated inductor using metaheuristic techniques

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