A modified ant colony optimization algorithm ACP&O used to solve Max/Min objective function: application in MPPT system

In engineering, researchers try to solve the technical problem modeled by an objective function by looking for the value corresponding to the minimum or the maximum. When the objective function has many LS (Local Solution, minima or maxima); the classical techniques algorithms fail to find the GS (Global Solution, minima or maxima) which is the desired solution to be attended. One of the most used solutions to find GS is the optimization technique inspired by animal behavior. Nonetheless, this algorithm has some drawbacks such as the high number of parameters that need to be tuned, high convergence time, and accuracy. This paper proposes a new hybrid method by taking advantage of the two algorithms ACO (Ant colony Optimization) and P&O (perturb and observe). This algorithm was tested on Maximum Power Point Tracking MPPT in a photovoltaic system in the presence of shading. According to the simulation obtained with MATLAB software, the suggested algorithm ACP&O outperformed the other conventional techniques, it then takes a little time to reach the Global solution with high precision.