Improved multi-objective particle swarm optimization algorithm

In order to enhance the convergence and diversity of multi-objective particle swarm optimization algorithm, an improved multi-objective particle swarm optimization algorithm was proposed. The Kent mapping was used to initialize the population, and the target space was divided into several fan-shaped regions evenly. A new diversity and convergence criteria was proposed to select the optimal solutions. An improved particle swarm update formula was used for global search. The clustering algorithm was used to analyze the angles between external population and the axis, and ensure the diversity of external population. Compared with the multi-objective particle swarm optimization algorithm and the nondominated sorting genetic algorithm II, the experiment of benchmark functions simulation verifies the effectiveness of the improved algorithm.