A multi-objective particle swarm optimization algorithm for dual elite selection

In this paper, a multi-objective particle swarm optimization algorithm (DEMOPSO) is proposed, which introduces a double elite selection mechanism to select high-quality elite particles in the archive and enhances the convergence and diversity of the population. In addition, the algorithm adjusts the degree of variation according to the particle crowding, which further enhances the diversity of the population and avoids the dilemma of local optimal solutions. Finally, by comparing and analyzing the results with the selected six classical comparison algorithms on the ZDT series of test functions, the experimental results verify that DEMOPSO performs well in terms of convergence and diversity and is able to achieve better optimization results in complex multi-objective optimization problems, demonstrating its superior performance and advantages.