Niche particle swarm optimization algorithm with integrated adaptive weibull flight

To address the inherent limitations of the standard Particle Swarm Optimization (PSO) algorithm, which is prone to falling into local optima, this paper proposes a Niche Particle Swarm Optimization algorithm with Adaptive Weibull Flight (NPSOAW). To enhance the global search capability of the algorithm, we design three progressive strategies, Firstly, an adaptive flight strategy based on the Weibull distribution is proposed to enhance the ability to escape from local optima through dynamic perturbation. Secondly, a quasi-opposition-based learning strategy is introduced to accelerate convergence efficiency by leveraging the symmetry of the solution space. Thirdly, a niche fitness sharing mechanism is combined to suppress the fitness allocation of particles in dense regions through a sharing radius, forcing the population to disperse and maintain diversity. Simulation experiments based on the CEC2019 benchmark test set show that NPSOAW significantly outperforms mainstream swarm intelligence optimization algorithms in terms of convergence accuracy and robustness. In the practical engineering problem of robot gripper design, its objective function value is better than that of the comparison algorithms, verifying the superiority of the algorithm in complex scenarios.