Particle Swarm Optimization with Aging Leader and Challengers for Optimal Design of Analog Active Filters

De, Bishnu Prasad; Kar, Rajib; Mandal, Durbadal; Ghoshal, Sakti Prasad

doi:10.1007/s00034-014-9872-8

Cited by 17 publications

(18 citation statements)

References 28 publications

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“…The first one, Figure A, is a fourth‐order Butterworth LPF, while the second one, Figure B, is a second‐order state variable LPF. These are the same filters studied in previous works using different evolutionary optimization methods. The goal is to choose the values of the passive components (resistors and capacitors) such that a specific LPF response is obtained.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Thus, the goal is to use SOS to find the values of the components (resistors and capacitors) that minimize the above fitness function, which results in the smallest design error. Following the approach in previous works, the components values are calculated as follows:

R_{1} = p \times 100 \times 10^{a} normalΩ R_{2} = q \times 100 \times 10^{b} normalΩ,

R_{3} = r \times 100 \times 10^{c} normalΩ R_{4} = s \times 100 \times 10^{d} normalΩ,

C_{1} = t \times 100 \times 10^{e} pF C_{2} = u \times 100 \times 10^{f} pF,

C_{3} = v \times 100 \times 10^{g} pF C_{4} = w \times 100 \times 10^{h} pF,

where [ p , q , r , s , t , u , v , w , a , b , c , d , e , f , g , h ] are real numbers that are the design variables. Thus, the dimension of the problem is 16.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Again, following the approach in previous works, the components values are calculated as follows:

R_{1} = p \times 100 \times 10^{a} normalΩ R_{2} = q \times 100 \times 10^{b} normalΩ,

R_{3} = r \times 100 \times 10^{c} normalΩ R_{4} = s \times 100 \times 10^{d} normalΩ,

R_{5} = t \times 100 \times 10^{e} normalΩ R_{6} = u \times 100 \times 10^{f} normalΩ,

C_{1} = v \times 100 \times 10^{g} pF C_{4} = w \times 100 \times 10^{h} pF,

where [ p , q , r , s , t , u , v , w , a , b , c , d , e , f , g , h ] are the design variables that are bounded as described in the Butterworth LPF design. It is worth mentioning that the transfer function of the state variable filter is

normalH ((), s) = \frac{\frac{R_{2} (R_{3} + R_{4})}{((), R_{3} R_{5} R_{6} C_{1} C_{2} ((), R_{1} + R_{2}))}}{s^{2} + \frac{R_{1} ((), R_{3} + R_{4})}{R_{3} R_{5} C_{1} ((), R_{1} + R_{2})} s + \frac{R_{4}}{R_{3} R_{5} R_{6} C_{1} C_{2}}} .

…”

Section: Problem Formulationmentioning

confidence: 99%

“…This procedure limits the flexibility of the design, even though it allows obtaining acceptable results with relatively reduced design effort. Recently, to make the design more reliable and flexible, different evolutionary optimization algorithms and techniques have been used in the design of active filters . In Jiang et al, the clonal selection algorithm was applied to obtain the optimal components values and reduce the design error of a fourth‐order Butterworth low‐pass filter (LPF).…”

Section: Introductionmentioning

confidence: 99%

“…In previous works, Vural et al applied several nature‐inspired metaheuristics (particle swarm optimization [PSO], differential evolution [DE], genetic algorithm, artificial bee colony [ABC], and harmony search) on the design of fourth‐order Butterworth LPF and second‐order state variable LPF. These same LPFs have been also studied in previous works using other evolutionary optimization methods, namely, the craziness‐based PSO (CRPSO), simplex PSO (SPSO), seeker optimization algorithm (SOA), DE, and aging leader and challenger PSO (ALC‐PSO). In each paper, it was shown that the used optimization method gives better results than other methods.…”

Section: Introductionmentioning

confidence: 99%

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Optimal design of analog active filters using symbiotic organisms search

Dib

El-Asir

2018

Int J Numerical Modelling

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This paper investigates the optimal design of analog active filters using the symbiotic organisms search (SOS) algorithm. Symbiotic organisms search is a newly proposed global optimization method that is inspired by the symbiotic interaction strategies between different organisms in an ecosystem. In SOS, the 3 common types of symbiotic relationships (mutualism, commensalism, and parasitism) are modeled using simple expressions, which are used to find the global minimum of the fitness function. Unlike other methods, SOS is free of tuning parameters, which makes it an attractive optimization method. Here, SOS is used to determine the values of the passive components (resistors and capacitors) used in active filters. Two active filter topologies, fourth‐order Butterworth low‐pass filter and second‐order state variable low‐pass filter, are considered. The components values are chosen from several standard industrial series (E12, E24, and E96). The results obtained using SOS are compared with those obtained using several optimization methods, like particle swarm optimization, seeker optimization algorithm, differential evolution, and other ones. It is shown that the SOS is a robust straightforward evolutionary algorithm that can outperform other well‐known methods.

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Section: Problem Formulationmentioning

confidence: 99%

R_{1} = p \times 100 \times 10^{a} normalΩ R_{2} = q \times 100 \times 10^{b} normalΩ,

R_{3} = r \times 100 \times 10^{c} normalΩ R_{4} = s \times 100 \times 10^{d} normalΩ,

C_{1} = t \times 100 \times 10^{e} pF C_{2} = u \times 100 \times 10^{f} pF,

C_{3} = v \times 100 \times 10^{g} pF C_{4} = w \times 100 \times 10^{h} pF,

where [ p , q , r , s , t , u , v , w , a , b , c , d , e , f , g , h ] are real numbers that are the design variables. Thus, the dimension of the problem is 16.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Again, following the approach in previous works, the components values are calculated as follows:

R_{1} = p \times 100 \times 10^{a} normalΩ R_{2} = q \times 100 \times 10^{b} normalΩ,

R_{3} = r \times 100 \times 10^{c} normalΩ R_{4} = s \times 100 \times 10^{d} normalΩ,

R_{5} = t \times 100 \times 10^{e} normalΩ R_{6} = u \times 100 \times 10^{f} normalΩ,

C_{1} = v \times 100 \times 10^{g} pF C_{4} = w \times 100 \times 10^{h} pF,

normalH ((), s) = \frac{\frac{R_{2} (R_{3} + R_{4})}{((), R_{3} R_{5} R_{6} C_{1} C_{2} ((), R_{1} + R_{2}))}}{s^{2} + \frac{R_{1} ((), R_{3} + R_{4})}{R_{3} R_{5} C_{1} ((), R_{1} + R_{2})} s + \frac{R_{4}}{R_{3} R_{5} R_{6} C_{1} C_{2}}} .

…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal design of analog active filters using symbiotic organisms search

Dib

El-Asir

2018

Int J Numerical Modelling

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Genetic algorithms applied to the design of robust biquadratic filters

Lovay

Romero

Peretti

2018

Int J Numerical Modelling

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This work presents a design methodology for discrete active RC biquadratic filters based on genetic algorithm (GA). The main difference with other methods is the consideration of the passive sensitivities during the optimization process.The GA delivers the values of resistors and capacitors (from E series) that comply with the desired specifications and minimize at the same time the sensitivities. This characteristic leads to filters that exhibit low deviations in their functional parameters, despite drifts in the values of passive components. Additionally, the method allows the designer to establish the gain in the passband. In this way, it overcomes the problem of low gain values observed in some of the solutions given by other authors. The paper presents an evaluation campaign that considers 12 designs for showing the robustness of the method. This study analyses the results delivered by the GA at transfer function level using MATLAB. Subsequent evaluations validate these results by using SPICE simulations. Following these procedures, it is observed a proper matching between the behaviors of the filters predicted by the GA and the simulated ones. The performance comparison with other heuristics (solving the same problem) shows that our proposal is well suited for the design of active filters. The stochastic nature of the GA allows obtaining different design alternatives in each run. This feature gives the designer the possibility to choose between several options that are more appropriate for a particular design problem. The paper also provides a simple guideline for dealing with the results from the iterative executions of the GA. The design method requires tools that are usual to find in the industry. Practitioners can straightforwardly implement it, even those that are nonspecialists in heuristics.

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