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spea2-sde's Introduction

SPEA2-SDE: Strength Pareto evolutionary algorithm 2 with shift-based density estimation

Reference: Li M, Yang S, Liu X. Shift-based density estimation for Pareto-based algorithms in many-objective optimization[J]. IEEE Transactions on Evolutionary Computation, 2013, 18(3): 348-365.
SPEA2-SDE is a multi-objective evolutionary algorithm (MOEA) and an improved version of SPEA2 with shift-based density estimation.
Variables Meaning
npop Population size
iter Iteration number
lb Lower bound
ub Upper bound
nobj The dimension of objective space (default = 3)
eta_c Spread factor distribution index (default = 20)
eta_m Perturbance factor distribution index (default = 20)
nvar The dimension of decision space
pop Population
objs The objectives of population
S The strength value
R The raw fitness
dom Domination matrix
D Density
fitness Fitness
mating_pool Mating pool
off Offspring
off_objs The objective of offsprings

Test problem: DTLZ1

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

Example

if __name__ == '__main__':
    main(100, 400, np.array([0] * 7), np.array([1] * 7))
Output:

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