In many engineering problems, an optimal solution must be derived by considering reality situations that reflect actual measured data. In the optimization problem, the objective function is modeled through a formula. Such modeling is difficult in reality situations that are affected by multiple environments, such as temperature and humidity. Therefore, this paper models the objective function as a data-driven approach under deep-learning-based nonlinear regression analysis, and we then propose the constraint optimization problem in a specific environment (trained DNN objective optimization). We define the solution to this problem as a controllable local-optimal solution (CLS) and propose an environment parameter fixed algorithm (EPFA) to derive the CLS. This changes constraint optimization into an unconstrained optimization problem by pinning down environment parameters to reflect constraints in a particular environment. After that, the proposed approach is combined with conventional Gradient Descent and algorithms such as Adagrad to derive the CLS. The situation is explained through an example of an optimal course model created for use in this study. In addition, we verify that the CLS can be derived through experiment by using an optimal course dataset and a Boston house price dataset.

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This paper addresses the challenge of optimizing objective functions in engineering problems influenced by multiple environmental factors, such as temperature and humidity. Traditional modeling approaches often struggle to capture the complexities of non-ideal situations. In this research, we propose a novel approach called the Environment Parameter Fixed Algorithm (EPFA) for optimizing the objective function of a deep neural network (DNN) trained in a specific environment. By fixing the environmental parameters in the DNN defined objective function, we transform the original optimization problem into a control parameter optimization problem. We integrate EPFA-CLS (Controllable local-Optimal Solution) with Gradient Descent and algorithms such as Adagrad to obtain the optimal solution. To demonstrate the concept, we apply our approach to an optimal course model and validate it using optimal course and Boston house price datasets. The results demonstrate the effectiveness of our approach in handling optimization problems in complex environments, offering promising outcomes for practical engineering applications.