An Inhomogeneous Grid-Based Evolutionary Algorithm for Many-Objective Optimization

Today, many-objective optimization problems have attracted widespread attention. There are significant advantages of the grid-based algorithm in solving multi-objective problems. Grid-based could offer a transformation objectives and further distinguish non-dominated solutions. However, grid not been fully exploited. For example, traditional homogeneous divisions can’t sufficiently reveal similarity adjacent And overemphasizing selection pressure may cause diversity decline grid. To exploit potentialities grid, an inhomogeneous evolutionary (named IGEA) is proposed. IGEA applies dynamic division approach redefining coordinate assignment individuals, which makes dominance relationship more obvious. also applied shift-based density estimation (SDE) strategy discriminating solutions coordinate. SDE can provide good balance convergence diversity. The compares with several state-of-the-art algorithms against regular irregular experimental results demonstrate that very competitive peer terms providing between