Fuzzy Evolutionary Algorithms and Genetic Fuzzy Systems: A Positive Collaboration between Evolutionary Algorithms and Fuzzy Systems

Herrera, Francisco; Lozano, Manuel

doi:10.1007/978-3-642-01799-5_4

Cited by 11 publications

(3 citation statements)

References 171 publications

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“…Step 7: repeating the abovementioned process until an optimal solution is obtained and the current solution is replaced by the optimal solution Te viability and efciency of the Jaya algorithm make it suitable for real-world problems such as feature selection, image processing, designing PID controllers, and many other applications [49][50][51]. Te algorithm is used to optimize multiple-objective cases such as (i) minimizing the total operating cost, (ii) minimizing the system loss, and (iii) minimizing voltage deviation.…”

Section: Covariance Matrix Adaptation Evolution Strategy (Cmaes) Niko...mentioning

confidence: 99%

Finite Impulse Response Filter Design Using Fuzzy Logic-Based Diversity-Controlled Self-Adaptive Differential Evolution

Kadhar¹,

Rengarajan

Tamilselvi

et al. 2023

International Transactions on Electrical Energy Systems

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The design of finite impulse response (FIR) filters involves the estimation of effective filter coefficients, making the designed filter exhibit infinite stopband attenuation and have a flat-shaped passband. The few conventional filter design methods such as impulse response truncation (IRT) and windowing technique exhibit undesirable characteristics owing to the Gibbs phenomenon, thus making them unsuitable for various practical complexities. This research work employs the fuzzy logic-based diversity-controlled self-adaptive differential evolution algorithm (FLDCSaDE) for the design of FIR band stop (BS) and high pass (HP) filters. In order to validate the results of the proposed technique, various population-based evolutionary computing techniques such as the covariance matrix adaptation evolution strategy (CMAES), differential evolution (DE), self-adaptive differential evolution (SaDE), and Jaya algorithm have also been applied for determining the effective filter coefficients. The performance of the various algorithms has been analysed and compared based on the parameters such as stopband attenuation, passband attenuation, and ripples. The simulation results show that the FLDCSaDE algorithm outperforms other evolutionary algorithms having 4% and 1.5% lower ripples than the SaDE algorithm for high pass and band stop filters, respectively. Experimental results depict that the performance of the fuzzy approach causes positioning and tracking accuracy obtained to be improved by 27% and the corresponding false positive rate (FPR) is substantially reduced to 0.11 from the mean amplitude value obtained from the fuzzy approach in the frequency response. The frequency response obtained from the FLDCSaDE algorithm is close to the ideal response of the BS and HP FIR filters.

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Section: Covariance Matrix Adaptation Evolution Strategy (Cmaes) Niko...mentioning

confidence: 99%

Finite Impulse Response Filter Design Using Fuzzy Logic-Based Diversity-Controlled Self-Adaptive Differential Evolution

Kadhar¹,

Rengarajan

Tamilselvi

et al. 2023

International Transactions on Electrical Energy Systems

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“…One of the main problems with fuzzy controllers is the adjustment of fuzzy sets which must be made for each linguistic variable (LV). According to a large number of studies (Ching-Chang et al, 2007;Kroeske et al, 2008;Herrera & Lozano, 2009), evolutionary computation has proved itself to be an effective tool for resolving this important design flaw of the FKBS. In addition, this way of thinking has already been applied in previous developments as a learning model (Usero et al, 2007).…”

Section: Related Workmentioning

confidence: 99%

Adaptive fuzzy knowledge‐based systems for control metabots' mobility on virtual environments

Arroyo¹,

Serradilla²,

Calvo³

2011

Expert Systems

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“…In [22], the GA was used for tuning the PI controller for load frequency control. Herrera and Lozano showed in [23] the benefits derived from the synergy between evolutionary algorithms and fuzzy logic systems.…”

Section: Introductionmentioning

confidence: 99%

Design of Fuzzy Fractional PD + I Controllers Tuned by a Genetic Algorithm

Jesus

Barbosa²

2014

Mathematical Problems in Engineering

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The fractional-order concepts are a useful tool to describe several physical phenomena, and nowadays they are widely used in the field of automatic control. A genetic algorithm (GA) is a search process for finding approximate solutions in optimization problems. The GA provides further flexibility and robustness that are unique for signal process. In this paper we consider the development of an optimal fuzzy fractional PD + I controller in which the parameters are tuned by a GA. The performance of the proposed fuzzy fractional control is illustrated through some application examples.

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Fuzzy Evolutionary Algorithms and Genetic Fuzzy Systems: A Positive Collaboration between Evolutionary Algorithms and Fuzzy Systems

Cited by 11 publications

References 171 publications

Finite Impulse Response Filter Design Using Fuzzy Logic-Based Diversity-Controlled Self-Adaptive Differential Evolution

Finite Impulse Response Filter Design Using Fuzzy Logic-Based Diversity-Controlled Self-Adaptive Differential Evolution

Adaptive fuzzy knowledge‐based systems for control metabots' mobility on virtual environments

Design of Fuzzy Fractional PD + I Controllers Tuned by a Genetic Algorithm

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