Electricity is essential in various settings for multiple purposes to facilitate a wide range of activities. The use of solar energy for electricity generation offers a practical answer to a variety of challenges. Solar panels are frequently employed to convert sunlight into electrical energy. Incorporating a maximum power point tracking circuit (MPPTC) into the photovoltaic system (PVS) is crucial for enhancing its efficiency and ensuring optimal utilization. The study selects a DC to DC forward converter to serve as the MPPTC. Numerous conventional algorithms, including incremental conductance and perturb & observe (P&O), can be effectively employed for MPPTC under stable solar irradiance conditions. Modules in PVS that are interconnected to create a power generation system may encounter fluctuations in irradiance due to various factors, including trees, shading, dust, birds, clouds, and other environmental influences. This event causes the system to undergo a Partial Shading Condition (PSC). Conventional algorithms are not capable of identifying the ideal location for optimizing energy harvesting under PSC condition. It is essential to adopt an optimization approach to enhance the efficiency of the PVS while operating under PSC conditions. The document presents a novel optimization technique referred to as the "Parrot Optimization Algorithm (POA)" aimed at enhancing energy harvesting during PSC for MPPTC. This document additionally includes a comparative analysis of the POA, Modified Invasive Weed Optimization (MIWO), Modified Grey Wolf Optimization (MGWO), and Whale Optimization Algorithm (WOA). The Hardware-in-the-Loop (HIL) system is established using OPAL-RT units to obtain a range of data for analysis across different operational scenarios.