IJCSNS International Journal of Computer Science and Network Security, VOL.20 No.3, March 2020 174 Manuscript received March 5, 2020 Manuscript revised March 20, 2020 Tuning PID using Particle Swarm Optimization For Controlling Temperature of The Infant-Incubator Jihed El Hadj Ali † , Elyes Feki † and Abdelkader Mami † , Faculty of Mathematical, Physical and Natural Science of Tunis, Application Laboratory, Energy Efficiency and Renewable Energy (LAPER), El Manar University, 2092, Tunisia. Summary To guarantee the survival of each child since his birth, especially the preterm babies, we must focus on improving care around the specific newborn, this subject is very important and has big attention for the biomedical company. However, in the developing countries the expensive and high price of those devices (intensive unit care), creates a big challenge to ameliorate Due to suffering from the performance of the typical PID controller in the commercial infant incubator. The Particle Swarm Optimization (PSO) appears as a successful optimization tool. The main research of this paper is to investigate the use of the Particle Swarm Optimization techniques for tuning the gains of the PID of the heater inside the infant-incubator to minimizing the temperature inside the Care Unit. To achieve the performance of this model. Several Computer simulations and experimental results prove that the performance of the optimal PID using the PSO controller gives a superior performance than that of the traditional design methods of the conventional PID controller. For establishing the optimal PID controller the use of the four performances index (IAE, ISE, ITAE, and ITSE) as the objective function. Key words: Infant-incubator, Particle Swarm Optimization, PSO, PID-Control, Temperature. 1. Introduction Until our days, the challenges in engineering have constantly viewed as a major inspiration to take care of the issues of augmenting gains or minimizing the losses. Besides, the obscurity of the optimization problems has been growing with the technology’s progress. For the purpose to solve this problem our paper focuses on the use of the PSO approach to control temperature air inside the infant incubator. In 1995, James Kennedy a social Psychologist and Russell Eberhart an Electrical engineer developed a new evolutionary computational technique known as Particle Swarm Optimization (PSO) for solving continuous and discrete optimization problems [1]. The approach is inspired by the work of Heppner and Grenander which they studied natures flocks of birds, schools of fish and swarms of insects [2, 3]. These ideas were developed into the Particle Swarm Optimizer. Since 1995, this approach witnessed the development of many applications and variants [4, 5]. A basic variant of the PSO algorithm works by having a population (called a swarm) of candidate solution (called particles). These particles are moved around in the search space according to a few simple formulae. The movements of the particles are guided by their own best- known position in the search-space as well as the entire swarm's best-known position. When improved positions are being discovered, these will then come to guide the movements of the swarm. The process is repeated and by doing so it is hoped, but not guaranteed, that a satisfactory solution will eventually be discovered. Here in this technique, a set of particles is put in a d-dimensional search space with a randomly chosen velocity and position. The initial position of the particle is taken as the best position for the start and then the velocity of the particle is updated based on the experience of other particles of the swarming population. It should be noted that the majority of neonatal incubators is controlled by a PID controller. In what follows, we will describe the different algorithms used in this process. As indicated by the World Health Organization (WHO), every year, 2.6 million babies pass away on in the initial 28 days of life. For the most part of them was dying in the first week of their life [6]. Numerous scholars have reported the infant incubator, but always they have bordered their research only on the mathematical model starting [7] in 1996 and it continued work on the same topic with [8, 9]. Then, with the study of [10] Gustavo H. C. Oliveira, the appearing of the use of the predictive control and there were several studies since 2010 to 2019 have interested about the control and the