A Many-Objective Evolutionary Algorithm Based on New Angle Penalized Distance

In evolutionary many-objective optimization, achieving better balance between convergence and diversity of the population is a crucial way to improve the efficiency of the algorithm. However, diversity measure may select the individuals having good diversity but degrade the convergence process to a certain extent. If the convergence measure focuses on the convergence of the individuals too much, it may lead to local convergence. The selection pressure achieves a severe loss, especially when the Pareto dominance selection mechanism is difficult to select solutions. To address these issues, a many-objective evolutionary algorithm based on new angle penalized distance is proposed in this paper, which is termed MaOEA-NAPD. In MaOEA-NAPD, it could dynamically balance the convergence and diversity of the population concerning their importance degree during the evolutionary process based on new angle penalized distance. In order to enhance the selection probability of better solutions in the mating pool, new convergence measure and diversity measure are introduced according to the achievement scalarizing function and angle based crowding degree estimation, respectively. The performance of the proposed method is evaluated and compared with five state-of-the-art algorithms on the WFG test suites with up to 15 objectives. Experimental results show the superior performance of MaOEA-NAPD than the compared algorithms on all the considered test instances.