Solving many-objective optimization problems has become one of the most popular research areas in recent years due to its ever-increasing applications in industries and other fields. In this paper, a novel two-phase hybrid feeder (TPHF) is proposed to provide elite solutions for selection mechanism of many-objective optimization algorithms (MaOAs) to improve their performance. The proposed TPHF framework generates solutions using a novel particle swarm optimization (PSO) operator along with a genetic algorithm operator during a two-phase calculated search based on the average velocity of the PSO particles. TPHF focuses on the worst solutions of the population to find a better place for them. Therefore, it frequently resets the PSO particles to the worst solutions. The new PSO operator uses the novel idea of dynamic inertia and learning factors and a novel velocity update equation. The classic global bests set of the classic PSO operator is replaced by a PSO feeder which uses the novel idea of using groups of the best/worst solutions to feed the new PSO operator based on the phase of the search. The proposed TPHF is applied to some of the most famous and state-of-the-art MaOAs with their corresponding default parameters. The result of the comparison between these MaOAs with their corresponding TPHF versions on MaF test suite shows a significant improvement in the performance of the TPHF versions in 62.2% of the cases.
