HYBRID SINE COSINE AND FITNESS DEPENDENT OPTIMIZER FOR INCOMPLETE DATASET
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 wei...
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| Main Authors: | , , |
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| Format: | Book Chapter |
| Language: | English |
| Published: |
Cambridge Scholars Publishing
2024
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/46905/1/Hybrid%20Sine%20Cosine%20and%20Fitness.pdf http://ir.unimas.my/id/eprint/46905/ https://www.cambridgescholars.com/product/978-1-0364-0804-6 |
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| Summary: | 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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