Evolutionary many-objective optimization algorithm based on angle and clustering

In evolutionary multi-objective optimization, maintaining a well balance of convergence and diversity is particularly important for the performance of evolutionary algorithms. Considering the convergence and diversity at the same time, a many-objective optimization algorithm combining angle-based selection strategy and clustering strategy is proposed. In the former strategy, the whole population is divided into several partitions to ensure the diversity of the population, and superior individuals are selected to ensure the convergence of the population. The latter strategy, the individual vector angle is used to reflect the similarity and the individuals are divided into some clusters, which helps to describe the population distribution. The performance of this algorithm is compared with five state-of-the-art evolutionary many-objective optimization algorithms on a variety of benchmark test problems with 5, 10 and 15 objectives. The results suggest that the algorithm can slightly better competitive performance.