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2 2020

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Toward the best combination of optimization with fuzzy systems to obtain the best solution for the GA and PSO algorithms using parallel processing

Valdez Fevrier, Kawano Yunkio, Melin Patricia

Journal of Automation Mobile Robotics and Intelligent Systems

2020

Vol. 14, No. 1

55--64

In general, this paper focuses on finding the best configuration for PSO and GA, using the different migration blocks, as well as the different sets of the fuzzy systems rules. To achieve this goal, two optimization algorithms were configured in parallel to be able to integrate a migration block that allow us to generate diversity within the subpopulations used in each algorithm, which are: the particle swarm optimization (PSO) and the genetic algorithm (GA). Dynamic parameter adjustment was also performed with a fuzzy system for the parameters within the PSO algorithm, which are the following: cognitive, social and inertial weight parameter. In the GA case, only the crossover parameter was modified.

Optimization of convolutional neural networks using the fuzzy gravitational search algorithm

Poma Yutzil, Melin Patricia, González Claudia I., Martínez Gabriela E.

Journal of Automation Mobile Robotics and Intelligent Systems

2020

Vol. 14, No. 1

109--120

This paper presents an approach to optimize a Convolutional Neural Network using the Fuzzy Gravitational Search Algorithm. The optimized parameters are the number of images per block that are used in the training phase, the number of filters and the filter size of the convolutional layer. The reason for optimizing these parameters is because they have a great impact on performance of the Convolutional Neural Networks. The neural network model presented in this work can be applied for any image recognition or classification applications; nevertheless, in this paper, the experiments are performed in the ORL and Cropped Yale databases. The results are compared with other neural networks, such as modular and monolithic neural networks. In addition, the experiments were performed manually, and the results were obtained (when the neural network is not optimized), and comparison was made with the optimized results to validate the advantage of using the Fuzzy Gravitational Search Algorithm.