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This paper is concerned with solving nonconvex optimization problems arising in various engineering sciences. In particular, we focus on the design of a robust flux estimator of induction machines and the optimal design of on-chip spiral inductors. To solve these problems, a recently developed optimization method, called the heuristic Kalman algorithm (HKA), is employed. The principle of HKA is to explicitly consider the optimization problem as a measurement process designed to give an estimate of the optimum. A specific procedure, based on the Kalman estimator, was developed to improve the quality of the estimate obtained through the measurement process. The main advantage of HKA, compared to other stochastic optimization methods, lies in the small number of parameters that need to be set by the user. Based on HKA a simple but effective design strategy for robust flux estimator and on-chip spiral inductors is developed. Numerical studies are conducted to demonstrate the validity of the proposed design procedure.