Many-objective evolutionary algorithm based on spatial distance and decision vector self-learning

In this paper, a new many-objective optimization evolutionary algorithm (MaOEA), namely Many-Objective Evolutionary Algorithm Based on Spatial Distance and Decision Vector Self-Learning (DVSLEA), is proposed for many-objective optimization. The core idea of the algorithm is to use spatial distance to influence the value of disturbance ratio and then affect the generation of offspring. In order to make the algorithm have a good distribution, distribution vector is introduced for the procedure of disturbance. Moreover, a self-learning process is corporated to ascertain the value of disturbance ratio. To evaluate the performance of DVSLEA, the DTLZ and WFG test suites with 3, 5, 8, 10, and 15 objectives are adopted. The experimental results indicate that DVSLEA shows superior performance over nine competitive evolutionary algorithms(MOEA/DD, NSGA-III, VaEA, SPEA2, SPEA2-SDE, MOEADAWA, onebyoneEA, PREA, RVEA), when solving most of the test problems used.