A modification to particle swarm optimization algorithm

In this paper, a modification strategy is proposed for the particle swarm optimization (PSO) algorithm. The strategy adds an adaptive scaling term into the algorithm, which aims to increase its convergence rate and thereby to obtain an acceptable solution with a lower number of objective function evaluations. Such an improvement can be useful in many practical engineering optimizations where the evaluation of a candidate solution is a computationally expensive operation and consequently finding the global optimum or a good sub‐optimal solution with the algorithm is too time consuming, or even impossible within the time available. The modified PSO algorithm was empirically studied with a suite of four well‐known benchmark functions, and was further examined with a practical application case, a neural‐network‐based modeling of aerodynamic data. The numerical simulation demonstrates that the modified algorithm statistically outperforms the original one.