Comparative Study of Bio-inspired Algorithms Applied in the Optimization of Fuzzy Systems

Miramontes, Ivette; Melín, Patricia; Prado-Arechiga, German

doi:10.1007/978-3-030-34135-0_15

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…This neural network is part of a neuro-fuzzy hybrid model (Melin et al 2018 , 2017 , 2019 ; Miramontes et al 2018b ) with which different results related to the blood pressure are obtained; different modules used in the proposed model have been optimized (Miramontes et al 2018a , 2020a , 2020b ; Guzmán et al 2019 ; Guzman et al 2017 ; Carvajal et al 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Fuzzy dynamic parameter adaptation in the bird swarm algorithm for neural network optimization

et al. 2022

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Fuzzy dynamic parameter adaptation has proven to be of great help when it is implemented in bio-inspired algorithms for optimization in different application areas, such as control, mathematical functions, classification, among others. One of the main contributions of this work is the proposed improvement of the Bird Swarm algorithm using a Fuzzy System approach, and we called this improvement the Fuzzy Bird Swarm Algorithm. Furthermore, we use a set of complex Benchmark Functions of the Congress on Evolutionary Computation Competition 2017 to compare the results between the original algorithm and the proposed improvement of the algorithm. The fuzzy system is utilized for the dynamic parameter adaptation of the C1 and C2 parameters of the Bird Swarm Algorithm. As a result, the Fuzzy Bird Swarm Algorithm has enhanced exploration and exploitation abilities that help in achieving better results than the Bird Swarm Algorithm. We additionally test the algorithm’s performance in a real problem in the medical area, using the optimization of a neural network to obtain the risk of developing hypertension. This neural network uses information, such as age, gender, body mass index, systolic pressure, diastolic pressure, if the patient smokes and if the patient has parents with hypertension. Hypertension is one of the leading causes of heart problems, which in turn are also one of the top causes of death. Moreover, these days it causes more complications and deaths in people infected with COVID-19, the virus of the ongoing pandemic. Based on the results obtained through the 30 experiments carried out in three different study cases, and the results obtained from the statistical tests, it can be concluded that the proposed method provides better performance when compared with the original method.

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Section: Methodsmentioning

confidence: 99%

Fuzzy dynamic parameter adaptation in the bird swarm algorithm for neural network optimization

et al. 2022

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“…If (Iteration is High) and (Diversity is High) then (Diffusion is Low). Equations ( 9) and ( 10) mathematically define the geometrical shape of the triangular functions for type-1 and type-2 fuzzy logic, respectively [33][34][35][36][37]. , , , , , , , ])…”

Section: Proposed Dynamic Stochastic Fractal Search (Dsfs)mentioning

confidence: 99%

A New Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation

et al. 2021

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Stochastic fractal search (SFS) is a novel method inspired by the process of stochastic growth in nature and the use of the fractal mathematical concept. Considering the chaotic stochastic diffusion property, an improved dynamic stochastic fractal search (DSFS) optimization algorithm is presented. The DSFS algorithm was tested with benchmark functions, such as the multimodal, hybrid, and composite functions, to evaluate the performance of the algorithm with dynamic parameter adaptation with type-1 and type-2 fuzzy inference models. The main contribution of the article is the utilization of fuzzy logic in the adaptation of the diffusion parameter in a dynamic fashion. This parameter is in charge of creating new fractal particles, and the diversity and iteration are the input information used in the fuzzy system to control the values of diffusion.

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“…To control the diffusion parameter, a fuzzy inference system was introduced which dynamically adjusts the diffusion of the particles with a range of [0 1], this fuzzy system for control has two inputs (iteration and diversity) and Eqs. 9 and 10 mathematically define the geometrical shape of the triangular functions, for type-1 and type-2 fuzzy logic, respectively [33]- [37].…”

Section: Proposed Dynamic Stochastic Fractal Search (Dsfs)mentioning

confidence: 99%

A new Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation

Lagunes¹,

Castillo²,

Valdez³

et al. 2021

Preprint

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Metaheuristic algorithms are widely used as optimization methods, due to their global exploration and exploitation characteristics, which obtain better results than a simple heuristic. The Stochastic Fractal Search (SFS) is a novel method inspired by the process of stochastic growth in nature and the use of the fractal mathematical concept. Considering the chaotic-stochastic diffusion property, an improved Dynamic Stochastic Fractal Search (DSFS) optimization algorithm is presented. The DSFS algorithm was tested with benchmark functions, such as the multimodal, hybrid and composite functions, to evaluate the performance of the algorithm with dynamic parameter adaptation with type-1 and type-2 fuzzy inference models. The main contribution of the article is the utilization of fuzzy logic in the adaptation of the diffusion parameter in a dynamic fashion. This parameter is in charge of creating new fractal particles, and the diversity and iteration are the input information used in the fuzzy system to control the values of diffusion.

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Comparative Study of Bio-inspired Algorithms Applied in the Optimization of Fuzzy Systems

Cited by 8 publications

References 24 publications

Fuzzy dynamic parameter adaptation in the bird swarm algorithm for neural network optimization

Fuzzy dynamic parameter adaptation in the bird swarm algorithm for neural network optimization

A New Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation

A new Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation

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