Modern-day world is facing problems such as, electricity generation deficiency, mounting energy demand, GHG (Greenhouse Gas) emissions, reliability and soaring prices. To resolve these issues, sustainable and renewable energy resources like SPV (Solar Photovoltaic) would be quite helpful. In this regard, the extraction of maximum power from SPV array in PSC (Partial Shading Weather Conditions) remains a challenge. Creation of multiple power peaks in the P-V (Power-Voltage) curve of a PV array due to partial shading, makes it difficult to track GMPP (Global Maximum Power Point) out of multiple power peaks known as LMPP (Local Maximum Power Points). Conventional algorithms are not able to perform in any condition other than UWC (Uniform Weather Condition). Nature inspired SC (Soft Computing) algorithms efficiently track the GMPP in PSC. The top performing SC algorithm named, FPA (Flower Pollination Algorithm) presents an efficient solution for GMPP tracking in PSCs. In this paper, the efficiency, accuracy and tracking speed of FPA algorithm is optimized. Comparison of the proposed OFPA (Optimized Flower Pollination Algorithm) and the existing FPAs is performed for zero shading condition, weak PSC, strong PSC, and changing weather conditions. In zero shading conditions, improvement of 0.7% in efficiency and 33% in tracking speed is achieved. In weak shading conditions, improvement of 0.97% in efficiency and 32.2% in tracking speed is achieved. In strong shading conditions, improvement of 0.24% in efficiency and 30.6% in tracking speed is achieved. OFPA is also tested for changing weather conditions (entering from Case-1 to Cae-3) and it retains its outstanding performance in the changing weather conditions. Simulations are performed in MATLAB/Simulink.