Modified Flower Pollination Algorithm for Disease Identification in Swine

Pigs have become an essential part of the cultural and economic life of the people in Nusa Tenggara
Timur (NTT) Province. Diseases in pigs significantly affect the success of pig farming. Identification of disease in
pigs is a classification problem. Metaheuristic algorithms are widely used in Neural Network (NN) optimization to
solve classification problems. Flower Pollination Algorithm (FPA) is grouped into a metaheuristic algorithm that has
been commonly used in optimization cases in the real world. To improve FPA performance, this study proposes
replacing the FPA step vector parameter, namely Levy distribution, with Newton Polynomial Quadratic Interpolation
(NPQI), known as Quadratic Interpolation Flower Pollination (QIFP). Quadratic Interpolation Flower Pollination
Neural Network (QIFPNN), Flower Pollination Neural Network (FPNN), Bat Neural Network (BANN), and Particle
Swarm Optimization Neural Network (PSONN) algorithms were used to train NN in real cases of disease identification in pigs, covering 11 diseases with 68 clinical symptoms. The results showed that the proposed algorithm, namely QIFPNN, outperformed FPNN, BANN, and PSONN in classification accuracy. QIFPNN is also able to improve classification accuracy and speed of convergence when compared to FPNN. QIFPNN and FPNN, respectively, provide 82.6159 % and 67.4766 % accuracy, and the training time is 6056.240 seconds and 6555.179 seconds. QIFPNN accuracy increased by 22.40%, and training time was 7.61 % faster. It concluded that QIFPNN
could be used as a complementary model in disease identification in pigs.