Simulation and Optimization of Multi-Objective Particle Swarm Algorithm Using Test Functions

The multi-objective particle swarm algorithm operates through dynamic updates of particle positions and velocities. By leveraging test functions, we can simulate this algorithm's behavior and optimize the simulation results. During implementation, different test functions (such as ZDT or DTLZ suites) and optimization strategies (like archive maintenance or Pareto dominance handling) can be employed to comprehensively evaluate algorithm performance. Key parameters including swarm size, inertia weight, acceleration coefficients, and leader selection mechanisms can be systematically adjusted through configuration files or function arguments. Additional factors like crowding distance computation and non-dominated sorting may be incorporated to enhance solution diversity. Through multiple test function simulations (e.g., sphere, Rosenbrock, or multi-modal functions) and optimization cycles utilizing fitness evaluation functions, we gain deeper insights into the algorithm's applicability and limitations, providing robust references for further research and practical applications. Code structure typically involves initialization routines, velocity/position update loops, boundary handling, and Pareto front visualization modules.