HYBRID SINE COSINE AND FITNESS DEPENDENT OPTIMIZER FOR INCOMPLETE DATASET

Chiu, Po Chan and Ali, Selamat and Kuok, King Kuok (2024) HYBRID SINE COSINE AND FITNESS DEPENDENT OPTIMIZER FOR INCOMPLETE DATASET. In: Metaheuristic Algorithms and Neural Networks in Hydrology. Cambridge Scholars Publishing, pp. 208-230. ISBN 978-1-0364-0804-6

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Abstract

The hybrid sine cosine and fitness dependent optimizer (SC-FDO) introduces four modifications to the original fitness dependent optimizer (FDO) algorithm to improve its exploit-explore tradeoff with a faster convergence speed. The modifications include a modified pace-updating equation, a random weight factor and global fitness weight strategy, a conversion parameter strategy, and a best solution-updating strategy. This chapter evaluates the generalization ability of the hybrid SC-FDO-based neural network (SC-FDONN) in handling missing data imputation challenges that exhibit different percentages of missingness. The hybrid SC-FDONN's performance was evaluated using hold-out and cross-validation techniques. The findings revealed that the SC-FDONN outperformed all the benchmarks by an average accuracy of 94.3%. Therefore, the hybrid optimizer, SC-FDONN, is an effective technique for handling different percentages of missing data problems.

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