The inconsistent irradiance, temperature, and unexpected behavior of the weather affect the output of photovoltaic (PV) systems, classified as partial or complex partial shading conditions. Under these circumstances, obtaining the maximum output power from PV systems becomes problematic. This paper proposes a population-based optimization model, the horse herd optimization algorithm (HOA), inspired by natural behavior, to solicit the maximum power under partial or complex partial shading conditions. It is an intelligent strategy inspired by the surprise pounce-chasing style of the horse herd model. The proposed technique outperforms the standard in different weather conditions, needs less computational time, and has a fast convergence speed and zero oscillations after reaching a power point’s maximum limit. A performance comparison of the HOA is achieved with conventional techniques, such as “perturb and observe” (P&O), the bio-inspired adaptive cuckoo search optimization (ACS), particle swarm optimization (PSO), and the dragonfly algorithm (DA). The following comparison of the presented scheme with the other techniques shows its better performance with respect to fast tracking and efficiency, as well as stability under disparate weather conditions and the ability to obtain maximum power with negligible oscillation under partial and complex shading.