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Jose Soria - One of the best experts on this subject based on the ideXlab platform.
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a high speed interval type 2 fuzzy system approach for Dynamic parameter Adaptation in metaheuristics
Engineering Applications of Artificial Intelligence, 2019Co-Authors: Oscar Castillo, Patricia Ochoa, Patricia Melin, Emanuel Ontiveros, Cinthia Peraza, Fevrier Valdez, Jose SoriaAbstract:Abstract Fuzzy Dynamic Adaptation of parameters in meta-heuristic algorithms has recently been shown to provide an improvement in efficiency with respect to meta-heuristic algorithms with static parameters. However, executing a fuzzy inference in each iteration represents an increase in the computational cost, and this is even more critical in the case of using Type-2 Fuzzy Logic systems. On the other hand, fuzzy Dynamic Adaptation with Type-2 Fuzzy Logic has shown better performance when compared with respect to Type-1 Fuzzy Logic in diverse areas of application; therefore, the goal of this paper is aimed at reducing the computational cost of Type-2 Fuzzy Logic processing for Dynamic Adaptation of parameters in meta-heuristic algorithms. To reduce the computational cost of processing the Interval Type-2 Fuzzy system for Dynamic Adaptation of metaheuristic parameters, the use of an approximation to the Continuous Karnik–Mendel method (CEKM) is proposed. The proposed approach provides an analytical approximation to the CEKM method, in this way reducing the computational cost of evaluating the Interval Type-2 Fuzzy System. The performance of the proposed approach was tested with five benchmark functions and with one benchmark control problem. The proposed approach was tested with two different meta-heuristic algorithms, the Differential Evolution algorithm (DE) and the Harmony Search algorithm (HS), in both cases achieving a reduction in the computational cost, while maintaining the performance with respect to the Type-2 Dynamic Adaptation of parameters with the conventional type reduction methods.
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differential evolution with Dynamic Adaptation of parameters based on a fuzzy logic augmentation approach
Journal of Ultrasound in Medicine, 2019Co-Authors: Oscar Castillo, Patricia Ochoa, Jose SoriaAbstract:This paper proposes an improvement to the Differential Evolution algorithm using a fuzzy logic augmentation. The main contribution is to Dynamically adapt the parameters of mutation (F) and crossover (CR) using a fuzzy system, with the aim that the fuzzy system calculates the optimal parameters of the differential evolution algorithm during execution for obtaining better solutions, in this way arriving to the proposed new fuzzy differential evolution algorithm. In this paper experiments are performed with a set of mathematical functions using the original algorithm and the proposed method. Based on a statistical comparison of the original and proposed method, we can state that the fuzzy differential evolution algorithm outperforms the original differential evolution method.
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a fuzzy logic approach for Dynamic Adaptation of parameters in galactic swarm optimization
IEEE Symposium Series on Computational Intelligence, 2016Co-Authors: Emer Bernal, Oscar Castillo, Jose SoriaAbstract:In this article we propose the use of fuzzy systems for Dynamic adjustment of parameters in the galactic swarm optimization (GSO) method. This algorithm is inspired by the movement of stars, galaxies and superclusters of galaxies under the force of gravity. GSO uses various cycles of exploration and exploitation phases to achieve a trade-off between the exploration of new solutions and exploitation of existing solutions. In this paper we proposed distinct fuzzy systems for the Dynamic Adaptation of the c 3 and c 4 parameters to measure the performance of the algorithm with 17 benchmark functions with different number of dimensions. In this paper a comparison was made between different variants to prove the efficacy of the method in optimization problems.
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water cycle algorithm with fuzzy logic for Dynamic Adaptation of parameters
Mexican International Conference on Artificial Intelligence, 2016Co-Authors: Eduardo Mendez, Oscar Castillo, Jose Soria, Patricia Melin, Ali SadollahAbstract:This paper describes the enhancement of the Water Cycle Algorithm (WCA) using a fuzzy inference system to adapt its parameters Dynamically. The original WCA is compared regarding performance with the proposed method called Water Cycle Algorithm with Dynamic Parameter Adaptation (WCA-DPA). Simulation results on a set of well-known test functions show that the WCA can be improved with a fuzzy Dynamic Adaptation of the parameters.
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a study of parameter Dynamic Adaptation with fuzzy logic for the grey wolf optimizer algorithm
Mexican International Conference on Artificial Intelligence, 2016Co-Authors: Luis Rodriguez, Oscar Castillo, Jose SoriaAbstract:The main goal of this paper is to present a general study of the Grey Wolf Optimizer algorithm. We perform tests to determine in the first part which parameters are candidates to be Dynamically adjusted and in the second stage to determine which are the parameters that have the greatest effect in the performance of the algorithm. We also present a justification and results of experiments as well as the benchmark functions that were used for the tests that are presented. In addition we are presenting a simple fuzzy system with the results obtained based on this general study.
Oscar Castillo - One of the best experts on this subject based on the ideXlab platform.
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a high speed interval type 2 fuzzy system approach for Dynamic parameter Adaptation in metaheuristics
Engineering Applications of Artificial Intelligence, 2019Co-Authors: Oscar Castillo, Patricia Ochoa, Patricia Melin, Emanuel Ontiveros, Cinthia Peraza, Fevrier Valdez, Jose SoriaAbstract:Abstract Fuzzy Dynamic Adaptation of parameters in meta-heuristic algorithms has recently been shown to provide an improvement in efficiency with respect to meta-heuristic algorithms with static parameters. However, executing a fuzzy inference in each iteration represents an increase in the computational cost, and this is even more critical in the case of using Type-2 Fuzzy Logic systems. On the other hand, fuzzy Dynamic Adaptation with Type-2 Fuzzy Logic has shown better performance when compared with respect to Type-1 Fuzzy Logic in diverse areas of application; therefore, the goal of this paper is aimed at reducing the computational cost of Type-2 Fuzzy Logic processing for Dynamic Adaptation of parameters in meta-heuristic algorithms. To reduce the computational cost of processing the Interval Type-2 Fuzzy system for Dynamic Adaptation of metaheuristic parameters, the use of an approximation to the Continuous Karnik–Mendel method (CEKM) is proposed. The proposed approach provides an analytical approximation to the CEKM method, in this way reducing the computational cost of evaluating the Interval Type-2 Fuzzy System. The performance of the proposed approach was tested with five benchmark functions and with one benchmark control problem. The proposed approach was tested with two different meta-heuristic algorithms, the Differential Evolution algorithm (DE) and the Harmony Search algorithm (HS), in both cases achieving a reduction in the computational cost, while maintaining the performance with respect to the Type-2 Dynamic Adaptation of parameters with the conventional type reduction methods.
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differential evolution with Dynamic Adaptation of parameters based on a fuzzy logic augmentation approach
Journal of Ultrasound in Medicine, 2019Co-Authors: Oscar Castillo, Patricia Ochoa, Jose SoriaAbstract:This paper proposes an improvement to the Differential Evolution algorithm using a fuzzy logic augmentation. The main contribution is to Dynamically adapt the parameters of mutation (F) and crossover (CR) using a fuzzy system, with the aim that the fuzzy system calculates the optimal parameters of the differential evolution algorithm during execution for obtaining better solutions, in this way arriving to the proposed new fuzzy differential evolution algorithm. In this paper experiments are performed with a set of mathematical functions using the original algorithm and the proposed method. Based on a statistical comparison of the original and proposed method, we can state that the fuzzy differential evolution algorithm outperforms the original differential evolution method.
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binary cat swarm optimization algorithm with Dynamic Adaptation of parameters based on fuzzy logic
Mexican International Conference on Artificial Intelligence, 2018Co-Authors: Trinidad Castro Villa, Oscar CastilloAbstract:This paper describes a modification of the binary cat swarm optimization algorithm (BCSO) based on fuzzy logic. Different fuzzy systems were considered to measure the performance of the algorithm with a set of benchmark mathematical functions with different population sizes. The original BCSO was used to compare in terms of performance with the proposed fuzzy versions of BCSO called FBCSO-W and FBCSO-SW.
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a new meta heuristics of optimization with Dynamic Adaptation of parameters using type 2 fuzzy logic for trajectory control of a mobile robot
Algorithms, 2017Co-Authors: Camilo Caraveo, Fevrier Valdez, Oscar CastilloAbstract:Fuzzy logic is a soft computing technique that has been very successful in recent years when it is used as a complement to improve meta-heuristic optimization. In this paper, we present a new variant of the bio-inspired optimization algorithm based on the self-defense mechanisms of plants in the nature. The optimization algorithm proposed in this work is based on the predator-prey model originally presented by Lotka and Volterra, where two populations interact with each other and the objective is to maintain a balance. The system of predator-prey equations use four variables (α, β, λ, δ) and the values of these variables are very important since they are in charge of maintaining a balance between the pair of equations. In this work, we propose the use of Type-2 fuzzy logic for the Dynamic Adaptation of the variables of the system. This time a fuzzy controller is in charge of finding the optimal values for the model variables, the use of this technique will allow the algorithm to have a higher performance and accuracy in the exploration of the values.
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a fuzzy logic approach for Dynamic Adaptation of parameters in galactic swarm optimization
IEEE Symposium Series on Computational Intelligence, 2016Co-Authors: Emer Bernal, Oscar Castillo, Jose SoriaAbstract:In this article we propose the use of fuzzy systems for Dynamic adjustment of parameters in the galactic swarm optimization (GSO) method. This algorithm is inspired by the movement of stars, galaxies and superclusters of galaxies under the force of gravity. GSO uses various cycles of exploration and exploitation phases to achieve a trade-off between the exploration of new solutions and exploitation of existing solutions. In this paper we proposed distinct fuzzy systems for the Dynamic Adaptation of the c 3 and c 4 parameters to measure the performance of the algorithm with 17 benchmark functions with different number of dimensions. In this paper a comparison was made between different variants to prove the efficacy of the method in optimization problems.
Joseph Natonio - One of the best experts on this subject based on the ideXlab platform.
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a 16 gb s backplane transceiver with 12 tap current integrating dfe and Dynamic Adaptation of voltage offset and timing drifts in 45 nm soi cmos technology
IEEE Journal of Solid-state Circuits, 2012Co-Authors: Gautam Gangasani, John F Bulzacchelli, Sergey V Rylov, T Beukema, David A Freitas, William R Kelly, Michael J Shannon, Jieming Qi, Hui H Xu, Joseph NatonioAbstract:This paper presents a 16-Gb/s 45-nm SOI CMOS transceiver for multi-standard backplane applications. The receiver uses a 12-tap DFE with circuit refinements for supporting higher data rates. Both the receiver and the transmitter use Dynamic Adaptation to combat parameter drift due to changing supply voltage and temperature. A 3-tap FFE is included in the source-series-terminated driver. The combination of DFE and FFE permits error-free NRZ signaling at 16 Gb/s over channels exceeding 30 dB loss. The 8-port core with two PLLs is fully characterized for 16 GFC and consumes 385 m W/link.
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a 16 gb s backplane transceiver with 12 tap current integrating dfe and Dynamic Adaptation of voltage offset and timing drifts in 45 nm soi cmos technology
Custom Integrated Circuits Conference, 2011Co-Authors: Gautam Gangasani, John F Bulzacchelli, Sergey V Rylov, T Beukema, David A Freitas, William R Kelly, Michael J Shannon, Jieming Qi, Hui H Xu, Joseph NatonioAbstract:This paper presents a 16-Gb/s 45-nm SOI CMOS transceiver for multi-standard backplane applications. The receiver uses a 12-tap DFE with circuit refinements for supporting higher data rates. Both the receiver and the transmitter use Dynamic Adaptation to combat parameter drift due to changing supply and temperature. A 3-tap FFE is included in the source-series-terminated driver. The combination of DFE and FFE permits error-free NRZ signaling at 16-Gb/s over channels exceeding 30dB loss. The 8-port core with two PLLs is fully characterized for 16GFC and consumes 385mW/link.
Leandro Dos Santos Coelho - One of the best experts on this subject based on the ideXlab platform.
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cauchy particle swarm optimization with Dynamic Adaptation applied to inverse heat transfer problem
Systems Man and Cybernetics, 2010Co-Authors: Viviana Cocco Mariani, Vagner Jorge Neckel, Rafael Bartnik Grebogi, Leandro Dos Santos CoelhoAbstract:The particle swarm optimization (PSO) algorithm is a member of the wide category of swarm intelligence methods for solving global optimization problems. Its basic idea is the simulation of simplified animal social behaviors such as fish schooling and bird flocking. PSO algorithms are attracting attentions in recent years, due to their ability of keeping good balance between convergence and diversity maintenance. Several attempts have been made to improve the performance of the original PSO algorithm. In this paper, a modified version of the original PSO based on Cauchy distribution and Dynamic Adaptation of inertia factor, named modified PSO (MPSO), is proposed. to estimate the unknown variables of an inverse heat transfer problem. To validate the optimization performance of the proposed MPSO, an inverse heat transfer problem is illustrated and the algorithm has to estimate its unknown variables. The results testify that the MPSO can perform well in an inverse heat transfer problem.
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differential evolution with Dynamic Adaptation of mutation factor applied to inverse heat transfer problem
Congress on Evolutionary Computation, 2010Co-Authors: Viviana Cocco Mariani, Vagner Jorge Neckel, Leonardo Dallegrave Afonso, Leandro Dos Santos CoelhoAbstract:In this paper a Modified Differential Evolution (MDE) is proposed and its performance for solving the inverse heat transfer problem is compared with Genetic Algorithm with Floating-point representation (GAF) and classical Differential Evolution (DE). The inverse analysis of heat transfer has some practical applications, for example, the estimation of radioactive and thermal properties, such as the conductivity of material with and without the temperatures dependence of diffusive processes. The inverse problems are usually formulated as optimization problems and the main objective becomes the minimization of a cost function. MDE adapts a concept originally proposed in particle swarm optimization design for the Dynamic Adaptation of mutation factor. Using a piecewise function for apparent thermal conductivity as a function of the temperature data, the heat transfer equation is able to estimate the unknown variables of the inverse problem. The variables that provide the beast least squares fit between the experimental and predicted time-temperatures curves were obtained. Numerical results for inverse heat transfer problem demonstrated the applicability and efficiency of the MDE algorithm. In this application, MDE approach outperforms the GAF and DE best solutions.
Gautam Gangasani - One of the best experts on this subject based on the ideXlab platform.
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a 16 gb s backplane transceiver with 12 tap current integrating dfe and Dynamic Adaptation of voltage offset and timing drifts in 45 nm soi cmos technology
IEEE Journal of Solid-state Circuits, 2012Co-Authors: Gautam Gangasani, John F Bulzacchelli, Sergey V Rylov, T Beukema, David A Freitas, William R Kelly, Michael J Shannon, Jieming Qi, Hui H Xu, Joseph NatonioAbstract:This paper presents a 16-Gb/s 45-nm SOI CMOS transceiver for multi-standard backplane applications. The receiver uses a 12-tap DFE with circuit refinements for supporting higher data rates. Both the receiver and the transmitter use Dynamic Adaptation to combat parameter drift due to changing supply voltage and temperature. A 3-tap FFE is included in the source-series-terminated driver. The combination of DFE and FFE permits error-free NRZ signaling at 16 Gb/s over channels exceeding 30 dB loss. The 8-port core with two PLLs is fully characterized for 16 GFC and consumes 385 m W/link.
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a 16 gb s backplane transceiver with 12 tap current integrating dfe and Dynamic Adaptation of voltage offset and timing drifts in 45 nm soi cmos technology
Custom Integrated Circuits Conference, 2011Co-Authors: Gautam Gangasani, John F Bulzacchelli, Sergey V Rylov, T Beukema, David A Freitas, William R Kelly, Michael J Shannon, Jieming Qi, Hui H Xu, Joseph NatonioAbstract:This paper presents a 16-Gb/s 45-nm SOI CMOS transceiver for multi-standard backplane applications. The receiver uses a 12-tap DFE with circuit refinements for supporting higher data rates. Both the receiver and the transmitter use Dynamic Adaptation to combat parameter drift due to changing supply and temperature. A 3-tap FFE is included in the source-series-terminated driver. The combination of DFE and FFE permits error-free NRZ signaling at 16-Gb/s over channels exceeding 30dB loss. The 8-port core with two PLLs is fully characterized for 16GFC and consumes 385mW/link.
