2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 1 , PP: 274-287, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
N. Durga Devi 1 * , Tirimula Rao Benala 2
Doi: https://doi.org/10.54216/JISIoT.180121
System users are increasingly interested in software correctness and efficiency checks prior to usage. Programmers in the twenty-first century are therefore making a conscious effort to create software that is more accurate, more efficient, and less prone to bugs. A software development model utilizing metaheuristic machine learning algorithms involves using metaheuristic optimization techniques to enhance various aspects of the software development lifecycle, such as optimizing machine learning models, hyperparameters, and even software architecture. This research propose novel technique in feature weight model based optimization in software development utilizing Meta heuristic ML method. Here the feature weight and feature selection is carried out for software model using support additive regression Laplacian score perceptron neural network. Then the software model parameter optimization is carried out using ant binary swarm component encoder optimization method. Simulation analysis is carried out in terms of training accuracy, MAR (Mean absolute residual), Mean balanced relative error (MBRE), F-measure.
Feature weight model , Software development , Meta heuristic , Machine learning model , Regression Laplacian score
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