Multi-objective Evolutionary Algorithm with Adaptive Fitting Dominant Hyperplane

Most of the existing multi-objective optimization algorithms try to evenly distribute all solutions in the objective space. But for the irregular Pareto front(PF), it is difficult to find the real PF. Aiming at the multi-objective optimization problem with complex PF, a multi-objective evolutionary algorithm for adaptive fitting dominant hyperplane (MOEA_DH) is developed. Before each iteration, non-dominated sorting is applied on all candidate solutions. Solutions in the first front are used to fit a hyperplane in the objective space, which is called the current dominant hyperplane(DH). DH reflects the evolution trend of the current generation of non-dominanted solutions and guides the rapid convergence of dominanted solutions. A new partial ordering relation determined by front number and crowding distance on DH is set. When solving CF benchmark problems from multi-objective optimization in IEEE Congress on Evolutionary Computation 2019, the experiments validate our advantages to get the PF with better convergence and diversity.