Abstract
This study introduces a novel strategy that can determine the optimal values of control parameters of a PSO. These optimal control parameters will be very valuable to all the online optimization problems where the convergence time and the failure convergence rate are vital concerns. The newly proposed strategy uses two nested PSO (NESTPSO) searching loops; the inner one contained the original objective function, and the outer one used the inner PSO as a fitness function. The control parameters and the swarm size acted as the optimization variables for the outer loop. These variables were optimized for the lowest premature convergence rate, the lowest number of iterations, and the lowest swarm size. The new proposed strategy can be used for all the swarm optimization techniques as well. The results showed the superiority of the proposed NESTPSO control parameter determination when compared with several state of the art PSO strategies.
Highlights
Strategy for Optimal Particle swarm optimization (PSO) ControlSwarm optimization techniques have displayed highly effective tracking of the optimal solutions in various applications
Recommendations high confidence and in getting the global optimal solution and a fast convergence rate, espeApplying the PSO to search for the optimal conditions in any optimization needs high confidence in getting the global optimal solution and a fast convergence rate, espe
Applying the PSO to search for the optimal conditions in any optimization needs high confidence in getting the global optimal solution and a fast convergence rate, especially in the online applications of the PSO
Summary
Swarm optimization techniques have displayed highly effective tracking of the optimal solutions in various applications. Based on the previous discussion, it was concluded that the performance of PSO techniques is highly affected by the values of PSO control parameters, especially in online applications where the confidence of capturing the GB and the convergence time are the crucial factors. This is a pioneer study that has introduced a strategy to determine the optimal PSO control parameters in the metaheuristic techniques for minimum PCR and the shortest convergence time that suits all the applications This new proposed strategy uses two nested PSO loops and is called NESTPSO. The stunning results obtained from using the NESTPSO in the optimization of many benchmark functions with different levels of complexity and the real-world application show a substantial reduction in convergence time and PCR compared to 10 state-of-the-art strategies This improvement gained from NESTPSO allows the use of the PSO in the online applications that need very fast and reliable convergence, such as the maximum power point tracker (MPPT) of the PV systems.
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