Research on Cooperative Handing of Multi-robotic Fish Based on Bionic Optimization Algorithm

This study explores the efficiency of the Particle Swarm Optimization (PSO) algorithm in optimizing fish swarm transportation tasks. Our findings demonstrate that PSO effectively reduces the number of particles and iterations, rapidly converging to a solution that approximates the real distribution ratio. The particles dynamically update their speed and direction, maintaining a record of all past trajectories to avoid redundancy. As the iterations progress, the search range narrows, facilitating each particle's access to comprehensive group data, which directs them purposefully towards the optimal distribution, optimizing resource use and operational efficiency. Crucially, parameter adjustments, particularly the influence factor phi and movement step size, are essential for fine-tuning the particles' speed and optimizing convergence, requiring precise calibration to avoid suboptimal local solutions.