Dr. Fevrier Valdez - Academia.edu (original) (raw)

Papers by Dr. Fevrier Valdez

Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications, 2020

The paper introduces the basis to control a simple planar quadrotor model in the tracking traject... more The paper introduces the basis to control a simple planar quadrotor model in the tracking trajectory problem. The dynamics of the model is developed and a control system is designed and implemented to tracking two different trajectories without obstacles. General control system contains a PD controller to drive altitude (motion in z direction), and a cascade control scheme to drive y position by controlling orientation of roll angle. Results of the error position and command variables on two different trajectories are analyzed.

Journal of Intelligent & Fuzzy Systems, 2020

In this paper, we are presenting a survey of research works dealing with Type-2 fuzzy logic contr... more In this paper, we are presenting a survey of research works dealing with Type-2 fuzzy logic controllers designed using optimization algorithms inspired on natural phenomena. Also, in this review, we analyze the most popular optimization methods used to find the important parameters on Type-1 and Type-2 fuzzy logic controllers to improve on previously obtained results. To this end have included a summary of the results obtained from the web of science database to observe the recent trend of using optimization methods in the area of optimal type-2 fuzzy logic control design. Also, we have made a comparison among countries of the network of researchers using optimization methods to analyze the distribution and impact of the papers.

Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, 2018

In this paper the Galactic Swarm Optimization (GSO) algorithm with the use of fuzzy systems for t... more In this paper the Galactic Swarm Optimization (GSO) algorithm with the use of fuzzy systems for the adaptation of the parameters in the GSO algorithm is proposed. This algorithm is inspired by the movement of stars, galaxies and superclusters of galaxies under the force of gravity. The GSO algorithm uses multiple cycles of exploration and exploitation phases to achieve a balance between exploring new solutions and exploiting existing solutions. In this work different fuzzy systems were designed for the dynamic adaptation of the c3 and c4 parameters to measure the operation of the algorithm with 7 mathematical functions with different number of dimensions. A statistical comparison was made between the different variants to test the performance of the method applied to optimization problems.

Advances in Intelligent Systems and Computing, 2018

In this paper we perform a comparison of the use of type-2 fuzzy logic in two bio-inspired method... more In this paper we perform a comparison of the use of type-2 fuzzy logic in two bio-inspired methods: Ant Colony Optimization (ACO) and Gravitational Search Algorithm (GSA). Each of these methods is enhanced with a methodology for parameter adaptation using interval type-2 fuzzy logic, where based on some metrics about the algorithm, like the percentage of iterations elapsed or the diversity of the population, we aim at controlling their behavior and therefore control their abilities to perform a global or a local search. To test these methods two benchmark control problems were used in which a fuzzy controller is optimized to minimize the error in the simulation with nonlinear complex plants.

Recent Advances of Hybrid Intelligent Systems Based on Soft Computing, 2020

Combining Interval Type-2 Fuzzy Logic Systems with metaheuristics has shown in most investigation... more Combining Interval Type-2 Fuzzy Logic Systems with metaheuristics has shown in most investigations that better results are obtained than with Type-1 Fuzzy Logic Systems. In this comparative study, experiments were carried out with Type-1 and Interval Type-2 Fuzzy Logic Systems, each one in combination with the Flower Pollination Algorithm. In the modification of parameters, with this combination of hybrid methods we carried out the comparative study. Previously, experiments were carried out with the flower pollination algorithm and the Type-1 Fuzzy Logic System (T1FLS), with the results of both methods, and we have concluded that better results are obtained with the hybrid method of Interval Type-2 Fuzzy Logic System (IT2FLS) and the Flower Pollination Algorithm (FPA).

Metaheuristic algorithms are widely used as optimization methods, due to their global exploration... more 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 ...

In this paper we consider the problem of optimizing ensemble neural networks for pattern recognit... more In this paper we consider the problem of optimizing ensemble neural networks for pattern recognition with Type-1 and Type-2 fuzzy logic for parameter adaptation in the gravitational search algorithm. The database to be used is of echocardiography images, since these images are very important in clinical echocardiography, and these images help the doctors to diagnose cardiac diseases, as well as to prevent this type of diseases in patient treatment.

SpringerBriefs in Applied Sciences and Technology, 2019

Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine, 2019

The ACO algorithm is an optimization algorithm, recognized for being very efficient in problems o... more The ACO algorithm is an optimization algorithm, recognized for being very efficient in problems of finding routes and planning paths in roads. In terms of the problem of the traveling salesman, ACO algorithm has been able to find optimal solutions to the problem, we want to make a comparison with the algorithms GA and SA, to determine which of these obtains better results.

Applied Sciences, 2020

This paper presents a study of two popular metaheuristics, namely differential evolution (DE) and... more This paper presents a study of two popular metaheuristics, namely differential evolution (DE) and harmony search (HS), including a proposal for the dynamic modification of parameters of each algorithm. The methods are applied to two cases, finding the optimal design of a fuzzy logic system (FLS) applied to the optimal design of a fuzzy controller and to the optimization of mathematical functions. A fuzzy logic controller (FLC) of the Takagi–Sugeno type is used to find the optimal design in the membership functions (MFs) for the stabilization problem of an autonomous mobile robot following a trajectory. A comparative study of the results for two modified metaheuristic algorithms is presented through analysis of results and statistical tests. Results show that, statistically speaking, optimal fuzzy harmony search (OFHS) is better in comparison to optimal fuzzy differential evaluation (OFDE) for the two presented study cases.

International Journal of Fuzzy Systems, 2020

This paper presents a comparative study between the firefly algorithm (FA) and the galactic swarm... more This paper presents a comparative study between the firefly algorithm (FA) and the galactic swarm optimization (GSO) method, where the performance of both methods is observed and tested in the optimization of a fuzzy controller for path tracking of an autonomous mobile robot. The main contribution of this work is finding the best method that generates an optimal vector of values for the membership function optimization of the fuzzy controller. This with the goal of improving the performance of the controller and thus the trajectory generated by the autonomous robot is closer to the desired trajectory. It should be noted that the fuzzy controller that is optimized is an interval type-2 fuzzy controller, which has a greater capability for managing uncertainty than a type-1 fuzzy controller. In this case, the limiting membership functions in the interval type-2 fuzzy sets are themselves type-1 fuzzy sets that define the footprint of uncertainty. Type-2 fuzzy controllers have been shown in previous works to handle better the control of robotic systems under noisy and dynamic conditions and this is why their optimal design is very important. Simulation results show that GSO outperforms FA in the optimal design of interval type-2 fuzzy controllers.

Recent Advances of Hybrid Intelligent Systems Based on Soft Computing, 2021

General Type-2 Fuzzy Logic in Dynamic Parameter Adaptation for the Harmony Search Algorithm, 2020

Axioms, 2019

Galactic swarm optimization (GSO) is a recently created metaheuristic which is inspired by the mo... more Galactic swarm optimization (GSO) is a recently created metaheuristic which is inspired by the motion of galaxies and stars in the universe. This algorithm gives us the possibility of finding the global optimum with greater precision since it uses multiple exploration and exploitation cycles. In this paper we present a modification to galactic swarm optimization using type-1 (T1) and interval type-2 (IT2) fuzzy systems for the dynamic adjustment of the c3 and c4 parameters in the algorithm. In addition, the modification is used for the optimization of the fuzzy controller of an autonomous mobile robot. First, the galactic swarm optimization is tested for fuzzy controller optimization. Second, the GSO algorithm with the dynamic adjustment of parameters using T1 fuzzy systems is used for the optimization of the fuzzy controller of an autonomous mobile robot. Finally, the GSO algorithm with the dynamic adjustment of parameters using the IT2 fuzzy systems is applied to the optimization ...

Axioms, 2019

Currently, we are in the digital era, where robotics, with the help of the Internet of Things (Io... more Currently, we are in the digital era, where robotics, with the help of the Internet of Things (IoT), is exponentially advancing, and in the technology market we can find multiple devices for achieving these systems, such as the Raspberry Pi, Arduino, and so on. The use of these devices makes our work easier regarding processing information or controlling physical mechanisms, as some of these devices have microcontrollers or microprocessors. One of the main challenges in speed control applications is to make the decision to use a fuzzy logic control (FLC) system instead of a conventional controller system, such as a proportional integral (PI) or a proportional integral-derivative (PID). The main contribution of this paper is the design, integration, and comparative study of the use of these three types of controllers—FLC, PI, and PID—for the speed control of a robot built using the Lego Mindstorms EV3 kit. The root mean square error (RMSE) and the settling time were used as metrics t...

Granular Computing, 2018

This paper describes a methodology based on optimal granularity allocation for fuzzy system desig... more This paper describes a methodology based on optimal granularity allocation for fuzzy system design, and the main contribution is a method, based on the Firefly Algorithm, to generate and test information granules for fuzzy controllers of autonomous mobile robots. The Firefly Algorithm automatically generates and tests these granules which are defined by the parameter values of the membership functions, which are evaluated based on simulations of the robot plant and the final result is an ideal combination of information granules. The evaluation is made with a comparison of the actual trajectory generated by the fuzzy controller of the robot with respect to the desired path. To verify that the obtained results are significantly better, a statistical test is performed between the firefly and the genetic algorithms.

2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016

In this work a new metaheuristic of optimization bio-inspired on the plants self-defense techniqu... more In this work a new metaheuristic of optimization bio-inspired on the plants self-defense techniques applied to optimization problems is presented. Plants are living beings that are part of a habitat, and in some recent works the authors claim that plants are able to react to different external stimuli. In nature, plants are exposed to a variety of predatory animals such as bacteria, fungi, insect predators in this case (herbivores), and the plants are required to develop different coping techniques to protect themselves from attacks by predators. The development for this approach we consider as a main idea the predator prey model of Lotka and Volterra, where two populations interact with each other and the objective is to maintain the balance between these two. The performance of this algorithm is tested on optimization problems of mathematical functions.

Studies in Computational Intelligence, 2016

According to the literature of particle swarm optimization (PSO), there are problems of local min... more According to the literature of particle swarm optimization (PSO), there are problems of local minima and premature convergence with this algorithm. A new algorithm is presented called the improved particle swarm optimization using the gradient descent method as operator of particle swarm incorporated into the Algorithm, as a function to test the improvement. The gradient descent method (BP Algorithm) helps not only to increase the global optimization ability, but also to avoid the premature convergence problem. The improved PSO algorithm IPSO is applied to Benchmark Functions. The results show that there is an improvement with respect to using the conventional PSO algorithm.

Studies in Computational Intelligence, 2016

The contribution of this paper is to provide an analysis of the parameters of Gravitational Searc... more The contribution of this paper is to provide an analysis of the parameters of Gravitational Search Algorithm (GSA), to include a fuzzy logic system for dynamic parameter adaptation through the execution of the algorithm, in order to control the behavior of GSA based on some metrics like the iterations and the diversity of the agents in an specific moment of its execution.

Applied Soft Computing, 2017

Intuitionistic and Type-2 Fuzzy Logic Enhancements in Neural and Optimization Algorithms: Theory and Applications, 2020

The paper introduces the basis to control a simple planar quadrotor model in the tracking traject... more The paper introduces the basis to control a simple planar quadrotor model in the tracking trajectory problem. The dynamics of the model is developed and a control system is designed and implemented to tracking two different trajectories without obstacles. General control system contains a PD controller to drive altitude (motion in z direction), and a cascade control scheme to drive y position by controlling orientation of roll angle. Results of the error position and command variables on two different trajectories are analyzed.

Journal of Intelligent & Fuzzy Systems, 2020

In this paper, we are presenting a survey of research works dealing with Type-2 fuzzy logic contr... more In this paper, we are presenting a survey of research works dealing with Type-2 fuzzy logic controllers designed using optimization algorithms inspired on natural phenomena. Also, in this review, we analyze the most popular optimization methods used to find the important parameters on Type-1 and Type-2 fuzzy logic controllers to improve on previously obtained results. To this end have included a summary of the results obtained from the web of science database to observe the recent trend of using optimization methods in the area of optimal type-2 fuzzy logic control design. Also, we have made a comparison among countries of the network of researchers using optimization methods to analyze the distribution and impact of the papers.

Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, 2018

In this paper the Galactic Swarm Optimization (GSO) algorithm with the use of fuzzy systems for t... more In this paper the Galactic Swarm Optimization (GSO) algorithm with the use of fuzzy systems for the adaptation of the parameters in the GSO algorithm is proposed. This algorithm is inspired by the movement of stars, galaxies and superclusters of galaxies under the force of gravity. The GSO algorithm uses multiple cycles of exploration and exploitation phases to achieve a balance between exploring new solutions and exploiting existing solutions. In this work different fuzzy systems were designed for the dynamic adaptation of the c3 and c4 parameters to measure the operation of the algorithm with 7 mathematical functions with different number of dimensions. A statistical comparison was made between the different variants to test the performance of the method applied to optimization problems.

Advances in Intelligent Systems and Computing, 2018

In this paper we perform a comparison of the use of type-2 fuzzy logic in two bio-inspired method... more In this paper we perform a comparison of the use of type-2 fuzzy logic in two bio-inspired methods: Ant Colony Optimization (ACO) and Gravitational Search Algorithm (GSA). Each of these methods is enhanced with a methodology for parameter adaptation using interval type-2 fuzzy logic, where based on some metrics about the algorithm, like the percentage of iterations elapsed or the diversity of the population, we aim at controlling their behavior and therefore control their abilities to perform a global or a local search. To test these methods two benchmark control problems were used in which a fuzzy controller is optimized to minimize the error in the simulation with nonlinear complex plants.

Recent Advances of Hybrid Intelligent Systems Based on Soft Computing, 2020

Combining Interval Type-2 Fuzzy Logic Systems with metaheuristics has shown in most investigation... more Combining Interval Type-2 Fuzzy Logic Systems with metaheuristics has shown in most investigations that better results are obtained than with Type-1 Fuzzy Logic Systems. In this comparative study, experiments were carried out with Type-1 and Interval Type-2 Fuzzy Logic Systems, each one in combination with the Flower Pollination Algorithm. In the modification of parameters, with this combination of hybrid methods we carried out the comparative study. Previously, experiments were carried out with the flower pollination algorithm and the Type-1 Fuzzy Logic System (T1FLS), with the results of both methods, and we have concluded that better results are obtained with the hybrid method of Interval Type-2 Fuzzy Logic System (IT2FLS) and the Flower Pollination Algorithm (FPA).

Metaheuristic algorithms are widely used as optimization methods, due to their global exploration... more 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 ...

In this paper we consider the problem of optimizing ensemble neural networks for pattern recognit... more In this paper we consider the problem of optimizing ensemble neural networks for pattern recognition with Type-1 and Type-2 fuzzy logic for parameter adaptation in the gravitational search algorithm. The database to be used is of echocardiography images, since these images are very important in clinical echocardiography, and these images help the doctors to diagnose cardiac diseases, as well as to prevent this type of diseases in patient treatment.

SpringerBriefs in Applied Sciences and Technology, 2019

Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine, 2019

The ACO algorithm is an optimization algorithm, recognized for being very efficient in problems o... more The ACO algorithm is an optimization algorithm, recognized for being very efficient in problems of finding routes and planning paths in roads. In terms of the problem of the traveling salesman, ACO algorithm has been able to find optimal solutions to the problem, we want to make a comparison with the algorithms GA and SA, to determine which of these obtains better results.

Applied Sciences, 2020

This paper presents a study of two popular metaheuristics, namely differential evolution (DE) and... more This paper presents a study of two popular metaheuristics, namely differential evolution (DE) and harmony search (HS), including a proposal for the dynamic modification of parameters of each algorithm. The methods are applied to two cases, finding the optimal design of a fuzzy logic system (FLS) applied to the optimal design of a fuzzy controller and to the optimization of mathematical functions. A fuzzy logic controller (FLC) of the Takagi–Sugeno type is used to find the optimal design in the membership functions (MFs) for the stabilization problem of an autonomous mobile robot following a trajectory. A comparative study of the results for two modified metaheuristic algorithms is presented through analysis of results and statistical tests. Results show that, statistically speaking, optimal fuzzy harmony search (OFHS) is better in comparison to optimal fuzzy differential evaluation (OFDE) for the two presented study cases.

International Journal of Fuzzy Systems, 2020

This paper presents a comparative study between the firefly algorithm (FA) and the galactic swarm... more This paper presents a comparative study between the firefly algorithm (FA) and the galactic swarm optimization (GSO) method, where the performance of both methods is observed and tested in the optimization of a fuzzy controller for path tracking of an autonomous mobile robot. The main contribution of this work is finding the best method that generates an optimal vector of values for the membership function optimization of the fuzzy controller. This with the goal of improving the performance of the controller and thus the trajectory generated by the autonomous robot is closer to the desired trajectory. It should be noted that the fuzzy controller that is optimized is an interval type-2 fuzzy controller, which has a greater capability for managing uncertainty than a type-1 fuzzy controller. In this case, the limiting membership functions in the interval type-2 fuzzy sets are themselves type-1 fuzzy sets that define the footprint of uncertainty. Type-2 fuzzy controllers have been shown in previous works to handle better the control of robotic systems under noisy and dynamic conditions and this is why their optimal design is very important. Simulation results show that GSO outperforms FA in the optimal design of interval type-2 fuzzy controllers.

Recent Advances of Hybrid Intelligent Systems Based on Soft Computing, 2021

General Type-2 Fuzzy Logic in Dynamic Parameter Adaptation for the Harmony Search Algorithm, 2020

Axioms, 2019

Galactic swarm optimization (GSO) is a recently created metaheuristic which is inspired by the mo... more Galactic swarm optimization (GSO) is a recently created metaheuristic which is inspired by the motion of galaxies and stars in the universe. This algorithm gives us the possibility of finding the global optimum with greater precision since it uses multiple exploration and exploitation cycles. In this paper we present a modification to galactic swarm optimization using type-1 (T1) and interval type-2 (IT2) fuzzy systems for the dynamic adjustment of the c3 and c4 parameters in the algorithm. In addition, the modification is used for the optimization of the fuzzy controller of an autonomous mobile robot. First, the galactic swarm optimization is tested for fuzzy controller optimization. Second, the GSO algorithm with the dynamic adjustment of parameters using T1 fuzzy systems is used for the optimization of the fuzzy controller of an autonomous mobile robot. Finally, the GSO algorithm with the dynamic adjustment of parameters using the IT2 fuzzy systems is applied to the optimization ...

Axioms, 2019

Currently, we are in the digital era, where robotics, with the help of the Internet of Things (Io... more Currently, we are in the digital era, where robotics, with the help of the Internet of Things (IoT), is exponentially advancing, and in the technology market we can find multiple devices for achieving these systems, such as the Raspberry Pi, Arduino, and so on. The use of these devices makes our work easier regarding processing information or controlling physical mechanisms, as some of these devices have microcontrollers or microprocessors. One of the main challenges in speed control applications is to make the decision to use a fuzzy logic control (FLC) system instead of a conventional controller system, such as a proportional integral (PI) or a proportional integral-derivative (PID). The main contribution of this paper is the design, integration, and comparative study of the use of these three types of controllers—FLC, PI, and PID—for the speed control of a robot built using the Lego Mindstorms EV3 kit. The root mean square error (RMSE) and the settling time were used as metrics t...

Granular Computing, 2018

This paper describes a methodology based on optimal granularity allocation for fuzzy system desig... more This paper describes a methodology based on optimal granularity allocation for fuzzy system design, and the main contribution is a method, based on the Firefly Algorithm, to generate and test information granules for fuzzy controllers of autonomous mobile robots. The Firefly Algorithm automatically generates and tests these granules which are defined by the parameter values of the membership functions, which are evaluated based on simulations of the robot plant and the final result is an ideal combination of information granules. The evaluation is made with a comparison of the actual trajectory generated by the fuzzy controller of the robot with respect to the desired path. To verify that the obtained results are significantly better, a statistical test is performed between the firefly and the genetic algorithms.

2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016

In this work a new metaheuristic of optimization bio-inspired on the plants self-defense techniqu... more In this work a new metaheuristic of optimization bio-inspired on the plants self-defense techniques applied to optimization problems is presented. Plants are living beings that are part of a habitat, and in some recent works the authors claim that plants are able to react to different external stimuli. In nature, plants are exposed to a variety of predatory animals such as bacteria, fungi, insect predators in this case (herbivores), and the plants are required to develop different coping techniques to protect themselves from attacks by predators. The development for this approach we consider as a main idea the predator prey model of Lotka and Volterra, where two populations interact with each other and the objective is to maintain the balance between these two. The performance of this algorithm is tested on optimization problems of mathematical functions.

Studies in Computational Intelligence, 2016

According to the literature of particle swarm optimization (PSO), there are problems of local min... more According to the literature of particle swarm optimization (PSO), there are problems of local minima and premature convergence with this algorithm. A new algorithm is presented called the improved particle swarm optimization using the gradient descent method as operator of particle swarm incorporated into the Algorithm, as a function to test the improvement. The gradient descent method (BP Algorithm) helps not only to increase the global optimization ability, but also to avoid the premature convergence problem. The improved PSO algorithm IPSO is applied to Benchmark Functions. The results show that there is an improvement with respect to using the conventional PSO algorithm.

Studies in Computational Intelligence, 2016

The contribution of this paper is to provide an analysis of the parameters of Gravitational Searc... more The contribution of this paper is to provide an analysis of the parameters of Gravitational Search Algorithm (GSA), to include a fuzzy logic system for dynamic parameter adaptation through the execution of the algorithm, in order to control the behavior of GSA based on some metrics like the iterations and the diversity of the agents in an specific moment of its execution.

Applied Soft Computing, 2017