Underwater multi-agent systems face critical hydrodynamic constraints that significantly degrade the performance of conventional constraint optimization algorithms in dynamic fluid environments. To meet the needs of underwater multi-agent applications, a fish recognition-inspired optimization method (FROM) is proposed in this paper. The proposed method introduces the characteristics of fish recognition. There are two major improvements in the proposed method: the neighbor topology improvement based on vision recognition and the learning strategies improvement based on hydrodynamic recognition. The computational complexity of the proposed algorithm was analyzed, and it was found to be acceptable. The statistical analysis of the experimental results shows that the FROM algorithm performs better than other algorithms in terms of minimum, maximum, standard deviation, mean, and median values calculated from objective functions. With solid experiment results, we conclude that the proposed FROM algorithm is a better solution to solve multi-agent decision-making problems with fluid environment constraints.

We proposed a pixel-based evolution method to automatically generate evolutionary art. Our method can generate diverse artworks, including original artworks and imitating artworks, with different artistic styles and high visual complexity. The generation process is fully automated. In order to adapt to the pixel-based method, a von Neumann neighbor topology-modified particle swarm optimization (PSO) is employed to the proposed method. The fitness functions of PSO are well prepared. Firstly, we come up with a set of aesthetic fitness functions. Next, the imitating fitness function is designed. Finally, the aesthetic fitness functions and the imitating fitness function are weighted into one single object function, which is used in the modified PSO. Both the original outputs and imitating outputs are shown. A questionnaire is designed to investigate the subjective aesthetic feeling of proposed evolutionary art, and the statistics are shown.

During the X-ray Diffraction (XRD) identification of the traditional Chinese medicine Os Draconis, the identification model often suffers from limited classification accuracy due to the difficulty in determining optimal parameters. To address this issue, this paper proposes a Hydrodynamic Improved Particle Swarm Optimization (HIIPSO) algorithm for the deep optimization of model parameters. In practical identification scenarios, the high complexity of XRD data poses severe challenges to the convergence speed and global search capability of optimization algorithms. To enhance model performance, this study introduces the interaction mechanism from fluid dynamics into the particle swarm optimization process. Specifically, HIIPSO incorporates a Voronoi neighbor topology to enhance population diversity and spatial distribution rationality. Concurrently, a hydrodynamic interaction mechanism is constructed to simulate the cooperative behavior of particles in a fluid environment, thereby effectively preventing the algorithm from falling into local optima. A theoretical analysis of the computational complexity of the HIIPSO algorithm in the parameter search task for XRD identification models was conducted, confirming that it falls within an ideal range for engineering applications. Statistical analysis of the experimental results demonstrates that, in the parameter optimization task for the Os Draconis identification model, the HIIPSO algorithm significantly outperforms traditional and other baseline algorithms across key metrics, including the optimal value, mean, standard deviation, and median of the objective function. The experimental data indicates that the HIIPSO algorithm can substantially improve the robustness and identification accuracy of the XRD-based Os Draconis identification model, making it an optimal solution for parameter optimization problems in the digital identification of complex mineral-based traditional Chinese medicines.