Multi-Objective Particle Swarm optimization Algorithm Based on Angle Preference and Three-Archive Sets

Multi-objective optimization problems (MOP) have not been completely solved due to their complexity. The evolutionary algorithm simulates the motor foraging mode of the biological group, which has certain advantages for solving the MOP, and can obtain the ε-pareto optimal solution. Particle swarm optimization (PSO) is well suitable for some evolutionary algorithms because of its fast convergence. Considering convergence, diversity and user preference information of multiple targets, we propose multi-objective particle swarm optimization algorithm with angle preference and three-archive sets (APTPSO). The validity of AP-TPSO is described by calculating the GD and SP values of the standard test functions.