Optimal population size of particle swarm optimization for photovoltaic systems under partial shading condition

<span>Particle swarm optimization (PSO) is the most widely used soft computing algorithm in photovoltaic systems to address partial shading conditions (PSC). The success of PSO run heavily depends on initial population size (NP). A higher NP increases probability a global peak (GP) solution, but at expense longer convergence time. To find optimal value NP, trade-off typically made between high rate and reasonable method trial-and-error approach that lacks explicit guidelines empirical evidence from detailed analysis, which can affect data reproducibility when different are used. Hence, this study proposes an based performance index (PI) indicator, takes into account weighted importance Furthermore, impact achieving successful was empirically investigated, with tested 16 NPs ranging 3 50, 10,000 independent runs various PSC problems. Overall, found best use 25, had average PI 0.9373 for solving all problems under consideration.</span>