A proactive metaheuristic model for optimizing weights of artificial neural network

Ali Hakem Alsaeedi, Ali Hussein Aljanabi, Mehdi Ebady Manna, Adil L. Albukhnefis


This paper proposes the  Particle Swarm Optimization model for enhancing the performance of an Artificial Neural Network. The learning process of Artificial Neural Network requires a long time to satisfy requirements because of processing complexity of the backpropagation algorithm that has been used in training Artificial Neural Network. It is a nonlinear complex model that can be used to configure and train an artificial neuron system. Both Artificial Neural Network and Particle Swarm Optimization model have been managed to solve and optimize several nonlinear models. Heuristic optimization weight of artificial neural network (HNN) is a proactive metaheuristic model proposed to optimize the performance of Artificial Neural Network. The proposed system applies Particle Swarm Optimization to find the optimum weights of the Artificial Neural Network instead of using the Backpropagation algorithm. Experimentally, the proposed system has required less processing time (average of 76.91 Sec.) than Backpropagation (average of 93.32 Sec). Furthermore, It has provided better classification accuracy (start from 80% to 97.20%) comparing with  Backpropagation (start from 75.32% to 94.32%).


Artificial neural network; Backpropagation algorithm; Data mining; Particle swarm optimization; Supervising learning

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DOI: http://doi.org/10.11591/ijeecs.v20.i2.pp976-984



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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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