Application of swarm intelligence techniques to the design of analog circuits: evaluation and comparison

Sallem, Amin; Benhala, Bachir; Kotti, Mouna; Fakhfakh, Mourad; Ahaitouf, Ali; Loulou, Mourad

doi:10.1007/s10470-013-0054-6

Cited by 44 publications

(18 citation statements)

References 48 publications

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“…SI metaheuristics are nowadays largely adopted for the resolution of similar optimization problems. Actually, it has been shown that when compared to notorious optimization algorithms, mainly genetic algorithms (GA) [26,33] and simulated annealing (SA) [35], SI techniques can be much interesting to be used because they can be more robust, faster, and require much less tuning of control parameters, see for instance [48].…”

Section: Resultsmentioning

confidence: 99%

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Computational Intelligence in Analog and Mixed-Signal (AMS) and Radio-Frequency (RF) Circuit Design

Fakhfakh¹,

Tlelo‐Cuautle²,

Siarry³

2015

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Section: Resultsmentioning

confidence: 99%

“…Nowadays, particle swarm optimization may be the most known and the most used technique, particularly in the analog and RF circuits and systems designs, see for instance [13,20,21,37,48,55].…”

Section: Pso and Bsa Metaheuristicsmentioning

confidence: 99%

Computational Intelligence in Analog and Mixed-Signal (AMS) and Radio-Frequency (RF) Circuit Design

Fakhfakh¹,

Tlelo‐Cuautle²,

Siarry³

2015

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“…This process can be performed by using metaheuristic algorithms especially the nature-inspired metaheuristic algorithms. Similar works, using metaheuristic algorithms to solve amplifier design problems, were performed by the following researchers for the given specific problems [9], [42]- [46]. Simple two-port microwave network which produces amplification with a proper design is shown in Fig.…”

Section: Microwave Amplifier Designmentioning

confidence: 99%

Core Levels Algorithm for Optimization: Case of Microwave Models

Haydar¹,

Ülker²,

Dimililer³

et al. 2017

ijacsa

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Abstract-Metaheuristic algorithms are investigated and used by many researchers in different areas. It is crucial to find optimal solutions for all problems under study especially for the ones which require sensitive optimization. Especially, for real case problems, solution quality and convergence speed of the algorithms are highly desired characteristics. In this paper, a new optimization algorithm called Core Levels Algorithm (COLA) based on the use of metaheuristics is proposed and analyzed. In the algorithm, two core levels are applied recursively to create new offsprings from the parent vectors which provides a desired balance on the exploration and exploitation characteristics. The algorithm's performance is first studied on some well-known benchmark functions and then compared with previously proposed efficient evolutionary algorithms. The experimental results showed that even at the early stages of optimization, obtained values are very close or exactly the same as the optimum values of the analyzed functions. Then, the performance of COLA is investigated on real case problems such as some selected microwave circuit designs. The results denoted that COLA produces stable results and provides high accuracy of optimization without high parameter dependency even for the real case problems.

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“…These techniques are based on animal behaviour and insect conduct, aiming to mimic their skills solving daily problems. In the area of Radio Frequency (RF) circuit design, Swarm Intelligence (SI) techniques are broadly applied, and among such techniques the PSO "Particle Swarm Optimization" [15][16][17], and the ACO "Ant Colony Optimization" [18][19][20][21], are the most used.…”

Section: Introductionmentioning

confidence: 99%

“…A study was conducted to the maximization of the quality factor Q and the minimization of the device area dout for the Radio-Frequency (RF) integrated inductor circuit [25][26][27][28][29][30]. Three algorithms for multi-objective Pareto optimization [31][32][33][34][35][36][37][38][39] are used. An evaluation concerning the performances of the three abovementioned multi-objective metaheuristics: the convergence and the robustness analyses are offered [40][41].…”

Section: Introductionmentioning

confidence: 99%

Multiple-Optimization based-design of RF Integrated Inductors

Houcine¹,

Sallem²,

Chaoui³

et al. 2019

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

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In this paper, a multiple-objective Metaheuristics study is discussed. Initially, three monoobjective metaheuristics will be explored in order to design and optimize Radio-Frequency integrated inductors. These metaheuristics are: An evolutionary algorithm called The Differential Evolution (DE), An algorithm supported on Newton's laws of gravity and motion called the Gravitational Search Algorithm (GSA) and, finally, A swarm intelligence algorithm called the Particle Swarm Optimization (PSO). The performances of these three mono-objective metaheuristics are evaluated and compared over three benchmark functions and one application to optimize the layout of a RF silicon-based planar spiral inductor, the double π-model is adopted. Secondly, three references multi-objective metaheuristics using Pareto front are used respectively the multi-objective PSO (MOPSO), the Pareto envelope-based selection algorithm-II (PESAII) and the multi-objective evolutionary algorithm based on decomposition (MOEA/D). The performances of these multi-objective optimization algorithms are evaluated and compared over two bi-objective benchmark functions and the same application used in the first section. Two conflicting performances were optimized, namely the quality factor 'Q' (to be maximized) and the device area 'dout' (to be minimized) for the RF inductor. It was concluded that the multipleobjective PSO are significantly more efficient and robust for difficult problems than the other metaheuristics.

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Application of swarm intelligence techniques to the design of analog circuits: evaluation and comparison

Cited by 44 publications

References 48 publications

Computational Intelligence in Analog and Mixed-Signal (AMS) and Radio-Frequency (RF) Circuit Design

Computational Intelligence in Analog and Mixed-Signal (AMS) and Radio-Frequency (RF) Circuit Design

Core Levels Algorithm for Optimization: Case of Microwave Models

Multiple-Optimization based-design of RF Integrated Inductors

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