A self-adaptive artificial bee colony algorithm with local search for TSK-type neuro-fuzzy system training

© 2019 IEEE. In this paper, we introduce a self-adaptive artificial bee colony (ABC) algorithm for learning the parameters of a Takagi-Sugeno-Kang-type (TSK-type) neuro-fuzzy system (NFS). The proposed NFS learns fuzzy rules for the premise part of the fuzzy system using an adaptive clustering method according to the input-output data at hand for establishing the network structure. All the free parameters in the NFS, including the premise and the following TSK-type consequent parameters, are optimized by the modified ABC (MABC) algorithm. Experiments involve two parts, including numerical optimization problems and dynamic system identification problems. In the first part of investigations, the proposed MABC compares to the standard ABC on mathematical optimization problems. In the remaining experiments, the performance of the proposed method is verified with other metaheuristic methods, including differential evolution (DE), genetic algorithm (GA), particle swarm optimization (PSO) and standard ABC, to evaluate the effectiveness and feasibility of the system. The simulation results show that the proposed method provides better approximation results than those obtained by competitors methods

Self Adaptive Artificial Bee Colony for Global Numerical Optimization

AbstractThe ABC algorithm has been used in many practical cases and has demonstrated good convergence rate. It produces the new solution according to the stochastic variance process. In this process, the magnitudes of the perturbation are important since it can affect the new solution. In this paper, we propose a self adaptive artificial bee colony, called self adaptive ABC, for the global numerical optimization. A new self adaptive perturbation is introduced in the basic ABC algorithm, in order to improve the convergence rates. 23 benchmark functions are employed in verifying the performance of self adaptive ABC. Experimental results indicate our approach is effective and efficient. Compared with other algorithms, self adaptive ABC performs better than, or at least comparable to the basic ABC algorithm and other state-of-the-art approaches from literature when considering the quality of the solution obtained

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Algoritmo de colonia de abejas artificiales hibridado con algoritmos evolutivos

In this paper, we design, implement, and analysis the replacement of the method to create new solutions in artificial bee colony algorithm by recombination operators, since the original method is similar to the recombination process used in evolutionary algorithms. For that purpose, we present a systematic investigation of the effect of using six different recombination operators for real-coded representations at the employed bee step. All the analysis is carried out using well known test problems. The experimental results suggest that the method to generate a new candidate food position plays an important role in the performance of the algorithm. Computational results and comparisons show that three of the six proposed algorithms are very competitive with the traditional bee colony algorithm.En este trabajo, se ha diseñado, implementado y analizado el reemplazo del método para crear nuevas soluciones en algoritmos basados en colonia de abejas artificiales por operadores de recombinación, ya que el método original es similar al proceso de recombinación usado en los algoritmos evolutivos. Para cumplir con este propósito, se presenta una investigación sistemática del efecto de usar seis operadores de recombinación distintos en el procedimiento llevado a cabo por la abeja empleada. Para la experimentación se utilizan casos de pruebas complejos, habitualmente utilizados en la literatura. Los resultados obtenidos sugieren que el método generador de nuevas fuentes de comida afecta el desempeño del algoritmo. A partir del análisis y comparaciones de los resultados, se observa que tres de las seis propuestas algorítmicas son competitivas con respecto al algoritmo basado en colonia de abejas tradicional.Facultad de Informátic

Algoritmo de colonia de abejas artificiales hibridado con algoritmos evolutivos

In this paper, we design, implement, and analysis the replacement of the method to create new solutions in artificial bee colony algorithm by recombination operators, since the original method is similar to the recombination process used in evolutionary algorithms. For that purpose, we present a systematic investigation of the effect of using six different recombination operators for real-coded representations at the employed bee step. All the analysis is carried out using well known test problems. The experimental results suggest that the method to generate a new candidate food position plays an important role in the performance of the algorithm. Computational results and comparisons show that three of the six proposed algorithms are very competitive with the traditional bee colony algorithm.En este trabajo, se ha diseñado, implementado y analizado el reemplazo del método para crear nuevas soluciones en algoritmos basados en colonia de abejas artificiales por operadores de recombinación, ya que el método original es similar al proceso de recombinación usado en los algoritmos evolutivos. Para cumplir con este propósito, se presenta una investigación sistemática del efecto de usar seis operadores de recombinación distintos en el procedimiento llevado a cabo por la abeja empleada. Para la experimentación se utilizan casos de pruebas complejos, habitualmente utilizados en la literatura. Los resultados obtenidos sugieren que el método generador de nuevas fuentes de comida afecta el desempeño del algoritmo. A partir del análisis y comparaciones de los resultados, se observa que tres de las seis propuestas algorítmicas son competitivas con respecto al algoritmo basado en colonia de abejas tradicional.Facultad de Informátic

Algoritmo de colonia de abejas artificiales hibridado con algoritmos evolutivos

In this paper, we design, implement, and analysis the replacement of the method to create new solutions in artificial bee colony algorithm by recombination operators, since the original method is similar to the recombination process used in evolutionary algorithms. For that purpose, we present a systematic investigation of the effect of using six different recombination operators for real-coded representations at the employed bee step. All the analysis is carried out using well known test problems. The experimental results suggest that the method to generate a new candidate food position plays an important role in the performance of the algorithm. Computational results and comparisons show that three of the six proposed algorithms are very competitive with the traditional bee colony algorithm.En este trabajo, se ha diseñado, implementado y analizado el reemplazo del método para crear nuevas soluciones en algoritmos basados en colonia de abejas artificiales por operadores de recombinación, ya que el método original es similar al proceso de recombinación usado en los algoritmos evolutivos. Para cumplir con este propósito, se presenta una investigación sistemática del efecto de usar seis operadores de recombinación distintos en el procedimiento llevado a cabo por la abeja empleada. Para la experimentación se utilizan casos de pruebas complejos, habitualmente utilizados en la literatura. Los resultados obtenidos sugieren que el método generador de nuevas fuentes de comida afecta el desempeño del algoritmo. A partir del análisis y comparaciones de los resultados, se observa que tres de las seis propuestas algorítmicas son competitivas con respecto al algoritmo basado en colonia de abejas tradicional.Facultad de Informátic