Considering the desired goal of shifting toward renewable energy by the world due to the swear issues of fossil fuel diminution that cause spiraling energy prices, and damage to the ozone layer due to the pollution caused by conventional energy-producing technologies and fuels, we have put our efforts in providing a solution to one of the most focused problems of solar renewable energy technology. Solar is one of the most focused and currently in use technology due to the reliable and free availability of the Sun as an energy source and the single-step conversion of sunlight into electricity (the most demanded form of energy) using the photoelectric effect. But its non-linear behavior bounds to avoid maximum power production due to the load. To solve the issue conventional algorithms came in and moved the operating power point to the position where maximum power could be delivered called maximum power point (MPP). However, under partial shading conditions tracking the MPP became impossible for conventional algorithms due to the disturbed power characteristic curve. The role of soft-computing algorithms begins here to efficiently track the real MPP among multiple arisen peaks in the power characteristic curve. One of the most prominent soft-computing algorithms is particle swarm optimization (PSO). It has the capability of efficiently tracking MPP but its weaknesses include complexity in structure, huge computations, hard implementation, and huge memory requirements. To overcome one or all existing weaknesses and improve the performance of the PSO algorithm we have modified its tracking strategy and found effective results.The proposed revised PSO algorithm has been evaluated on the 3S PV system, specifically designed for the comparison of conventional and proposed PSO algorithms in MATLAB/Simulink.