xavier-mayiming / sra2 Goto Github PK

$$ \begin{aligned} & k = nvar - nobj + 1, \text{ the last $k$ variables is represented as $x_M$} \\ & g(x_M) = 100 \left[|x_M| + \sum_{x_i \in x_M}(x_i - 0.5)^2 - \cos(20\pi(x_i - 0.5)) \right] \\ & \min \\ & f_1(x) = \frac{1}{2}x_1x_2 \cdots x_{M - 1}(1 + g(x_M)) \\ & f_2(x) = \frac{1}{2}x_1x_2 \cdots (1 - x_{M - 1})(1 + g(x_M)) \\ & \vdots \\ & f_{M - 1}(x) = \frac{1}{2}x_1(1 - x_2)(1 + g(x_M)) \\ & f_M(x) = \frac{1}{2}(1 - x_1)(1 + g(x_M)) \\ & \text{subject to} \\ & x_i \in [0, 1], \quad \forall i = 1, \cdots, n \end{aligned} $$

if __name__ == '__main__':
    main(100, 300, np.array([0] * 7), np.array([1] * 7))

The upper figure is the Pareto front obtained by the SRA, and the lower one is the SRA2. It can be seen that SRA2 can obtain more evenly distributed Pareto optimal solutions.