A reference vector-interpolation-based multi-objective evolutionary algorithm

Decomposition-based multi-objective evolutionary algorithm can achieve good convergence and diversity performance in solving many multi-objective optimisation problems, but it is difficult to ensure good diversity of pareto optimal solutions in some multi-objective optimisation problems with complex pareto fronts, such as fronts with large curvature or discontinue fronts. If pareto solutions are not uniformly distributed on the front surface, and there are many invalid reference vectors. To solve such problems, a multi-objective evolutionary algorithm based on reference vector interpolation is proposed in this paper. In the proposed method, the MOEAD algorithm is firstly used to obtain the initial pareto frontier, and the obtained pareto solution is clustered to obtain multiple clustering subsets. Then, is a nonlinear fitting is performed in the objective space to construct a pseudo-pareto frontier to approximate the subsets, and a new weight vector is generated by using uniform interpolation on the pseudo-pareto frontier. Finally, the new weight vector is used to update the current reference vector to push the population to move to the optimal pareto frontier and promote the convergence of the evolutionary process. In order to verify the effectiveness and advantage, the proposed algorithm is compared with nine existing multi-objective evolutionary algorithms on 17 test problems. The results indicate that the proposed algorithm can achieve better diversity of pareto-optimal solutions on most test problems.