Many-objective evolutionary optimization based on reference points

This figure illustrates the flowchart of the proposed reference points-based evolutionary algorithm (RPEA). Its basic procedure is similar to most generational multi-objective evolutionary algorithms. First, an initial population is formed by randomly generating individuals. Then, genetic operators are performed to obtain an offspring population. Next, a set of reference points is generated based on the combined population. Finally, superior solutions are selected according to the reference points to update the parent population. It can be seen that there are two key operators in RPEA: generation of reference points and selection of individuals. In this study, reference points with good performances in convergence and distribution are generated by making full use of information provided by the current population. In addition, superior individuals are selected based on the evaluation of each individual by calculating the distances between the reference points and the individual in the objective space. 149