There are many probabilistic algorithms used to get the global optimal solution for all nonlinear problems and ant colony optimization (ACO) is one of these. . The ACO was implemented in different studies [23, 24], has been formulated to operate continuously, and can be easily adjusted to changing in environmental conditions. The main benefit of ACO’s is its need of only one combination of voltage and current sensors that increases the system’s reliability at considerably lower cost. This also increases the PV system’s efficiency even though it is not applied to the distributed MPPT controllers. It has a set of associated parameters with graph components (either nodes or edges) and values of the components can be modified at runtime by the ants. The block diagram of the proposed system is shown in Figure 2.
The probability of an ant to move from node i to j is given below:
Tij : is the amount of pheromone on edge i, j;,
α : a parameter to control the influence of Tij,
ηij : is the desirability of edge i j (typically 1/dij),
β : is a parameter to control the influence of ηij.
The variation in amount of pheromone is recorded using the following equation:
Tij = ρ.Tij (t - 1) + ΔTij / t = 1, 2, 3...T
ρ : Pheromone concentration rate (0-1).
ΔTij : is the amount of pheromone deposited.