Metaheuristic optimization in neural network model for seasonal data

Budi Warsito, Rukun Santoso, Hasbi Yasin


This research was focused on the use of metaheuristic optimization in neural network for time series modeling. The three optimization methods were used as experiments, i.e Genetic Algorithm, Particle Swarm Optimization, and Modified Bee Colony. Neural network architecture chosen in this research was Feed Forward Neural Network. The weaknesses and limitations of gradient-based methods inspired many researchers to try to use other methods. Non-gradient based method is a reasonable choice considering the learning algorithm. Neural network is inspired by the characteristics of creatures, so that optimization methods that also resemble the patterns of life in nature will be appropriate. In this paper, the three metaheuristic optimization methods were tried with various scenarios for getting the best one. The proposed procedure was applied to the rainfall data. The experimental study showed that Genetic Algorithm and Particle Swarm Optimization were recommended as optimization methods at Feed Forward Neural Network (FFNN) model for the rainfall data.


Metaheuristic; Neural network; Optimization; Rainfall; Time series;


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