Particle swarm optimization with fitness adjustment parameters

Particle swarm optimization (PSO) has been widely applied in solving optimization problems because of its simple execution with fast convergence and high solution quality as known. Previous research has observed the effect of PSO parameters on a particle's movement and the solution performance. Most PSO variants constructed the search strategy of evolution by controlling the movement step. The movement steps of the particles should usually be large in early evolution for exploration and become smaller in the late evolution for exploitation. Therefore, this study proposes a novel PSO algorithm based on the fitness performance (PSOFAP) of particles for rapid convergence to an approximate optimal solution. The experiment is verified through twelve benchmark problems and the results are compared with those of other PSO variants. Furthermore, a well-known nonparametric statistical analysis method, namely the Wilcoxon signed rank test, is applied to demonstrate the performance of the proposed PSOFAP algorithm. The results of the experiment and statistical analysis show that PSOFAP is effective in enhancing the convergence speed, increasing the solution quality, and accurately adapting the parameter value without performing parametric sensitivity analysis.