Improving 1by1EA to Handle Various Shapes of Pareto Fronts

1by1EA is a competitive method among existing many-objective evolutionary algorithms. However, we find that it may fail to find boundary solutions depending on the Pareto front shape. In this study, we present an improved version of 1by1EA, named 1by1EA-II, to enhance the flexibility in handling various shapes of Pareto fronts. In 1by1EA-II, the Chebyshev distances from a solution to the nadir and ideal points are alternately employed as two convergence indicators. Using the first convergence indicator, boundary solutions are preferred for a wide spread in the objective space. With the other convergence indicator, non-boundary solutions are preferred to promote diversity. We empirically compare the proposed 1by1EA-II with its original version as well as four other state-of-the-art algorithms on DTLZ and Minus-DTLZ test problems. The results show that 1by1EA-II is the most flexible algorithm.

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