The selection of optimal design parameters for low-cost small transformers possesses significant challenges due to nonlinear objective functions and multiple constraints. Conventional optimization approach often fails to give global optima, necessitating the exploration of non-classical methods. Existing optimization schemes for single-phase transformer design parameter selection suffer from limitations, including convergence to local optima and inability to handle nonlinear objective functions, resulting in sub optimal material costs and efficiency.

This study aims to develop an efficient algorithm for minimizing material costs in a 5KVA, 230/115 volt, single-phase, core-type, dry transformer using Genetic Algorithm (GA), Simulated Annealing (SA), and Pattern Search (PS).

The total cost of copper and iron is considered the objective function. A comparative performance evaluation of GA, SA, and PS is conducted to identify the most effective optimization scheme. The results demonstrate that non-classical techniques outperform traditional methods, yielding improved and acceptable solutions. The optimal design parameters obtained using GA, SA, and PS are analyzed and compared.

This study establishes the efficacy of stochastic optimization methods in transformer design optimization, providing valuable insights for researchers and engineers. The findings suggest that GA, SA, and PS can be effectively employed to minimize material costs and enhance transformer efficiency.