# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 1 Introduction  
Parameters often take key roles in determining the accuracy and efficiency of algorithms, logic, and models for practical applications. Some parameters are sensitive to influence the program running efficiency, the calculation results, and the estimation accuracies of them. Therefore, the values of such sensitive parameters should be properly selected before running the programs for algorithms, logic, or models under development, which is usually processed manually by repeating the change of values and running the program many times.  
Previously, we have proposed a general-purpose _parameter optimization algorithm_ and studied its applications in various practical problems. This algorithm optimizes the parameter values by repeating small changes of them based on a local search method such that the _given score function_ be maximized (minimized). To avoid the local minimum convergence, the _tab_ search and _hill-climbing_ procedures are adopted together. The _score function_ should be designed for each problem to indicate the goodness (badness) of the current parameter values numerically.  
In this paper, we review the parameter optimization algorithm, which is named _paraOpt_ for convenience, and discuss its applications to three diverse problems in differentfields. Currently, deep learning approaches such as the convolutional neural network (CNN) and the recurrent neural network (RNN) become popular. Although these approaches may give powerful solutions to some problems such as pattern recognition and classifications, they are basically _black-box_ approaches. The users cannot validate the correctness of the model structure and modify it when mistakes or errors occur. Therefore, the _white-box_ approach of optimizing the parameter values in the comprehensible logical model is essential for practical use.  
The first application is the fingerprint-based indoor localization system using _IEEE802.15.4 devices_ called _FILS15.4_[1]. _FILS15.4_ adopts _IEEE802.15.4_ devices in _Mono Wireless_[2]. The transmitter is inexpensive, small, light, and has long battery life. Thus, this device is suitable to be always be worn by the user during location detections. The output signal from the transmitter is received at multiple receivers that are located beforehand in the field. The received signal strength, _LQI (link quality indicator)_, is compared with the standard LQI called the _fingerprint_, which has been registered for each possible location in the server. Then, the least different fingerprint is selected where the corresponding location is output as the current location of the user. _paraOpt_ optimizes the number of fingerprints for each location, the fingerprint values, and the detection cycle.  
The second application is the human face contour approximation model. This model has been developed to assist beginners to draw the face portrait of a person by referring to his/her face image. Drawing the face contour can be the first step to drawing the face portrait. This model consists of two half circles and two line segments since it can be easily drawn by beginners while it can well approximate the face contour. _paraOpt_ optimizes the center coordinates and the radii of the half circles.  
The third application is the computational fluid dynamic (CFD) simulation model. This model has been used to estimate temperature changes in rooms when some actions are performed, such as turning on/off air conditions and opening/closing doors/windows. _paraOpt_ optimizes the CFD model parameters and the boundary conditions. It is noted that slight changes in parameters could influence simulation results.  
The rest of this paper is organized as follows: Section 2 introduces related works. Section 3 reviews _paraOpt_. Sections 4-6 present the applications to _FILS15.4_, the face contour approximation model, and the CFD model, respectively. Section 7 discusses the results for three applications. Section 8 concludes this paper with future works.