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moea_d-iepsilon's Introduction

MOEA/D-IEpsilon: Multi-objective evolutionary algorithm based on decomposition and improved epsilon constraint-handling mechanism

Reference: Fan Z, Li W, Cai X, et al. An improved epsilon constraint-handling method in MOEA/D for CMOPs with large infeasible regions[J]. Soft Computing, 2019, 23: 12491-12510.
MOEA/D-IEpsilon is a constrained multi-objective evolutionary algorithm (CMOEA). The epsilon level is adjust according to the ratio of feasible to total solutions in the current population.
Variables Meaning
npop Population size
iter Iteration number
lb Lower bound
ub Upper bound
T Neighborhood size (default = 30)
delta The probability of selecting individuals in the neighborhood (default = 0.9)
nr The maximal number of solutions replaced by a child (default = 2)
tau Control the scale factor multiplied by the maximum overall constraint violation (default = 0.1)
alpha Control the searching preference between the feasible and infeasible regions (default = 0.95)
Tc Control generation (default = 0.8 * iter)
CR Crossover rate (default = 1)
F Mutation scalar number (default = 0.5)
pm Mutation probability (default = 1)
eta_m Spread factor distribution index (default = 20)
nvar The dimension of decision space
nobj The dimension of objective space
V Weight vectors
B The T closet weight vectors
pop Population
objs Objectives
phi Constraint violations
phi_max The maximum overall constraint violation found so far
rk The ratio of feasible solutions to all solutions
z Ideal point
NS Feasible non-dominated solutions
rp Random permutation
off Offspring
off_obj Offspring objecrtive
off_phi Offspring constraint violation
c Update counter

Test problem: LIR-CMOP6

$$ \begin{aligned} & J_1={3, 5, \cdots, 29}, J_2 = {2, 4, \cdots, 30} \\ & g_1(x) = \sum_{i \in J_1} (x_i - \sin(0.5i\pi x_1/30))^2 \\ & g_2(x) = \sum_{i \in J_2} (x_i - \cos(0.5i\pi x_1/30))^2 \\ &\min \\ & f_1(x) = x_1 + 10g_1(x) + 0.7057 \\ & f_2(x) = 1 - x_1^2 + 10g_2(x) + 0.7057 \\ & \text{subject to} \\ & c_k(x) = ((f_1 - p_k)\cos\theta - (f_2 - q_k)\sin\theta)^2/a_k^2 + ((f_1 - p_k)\sin\theta - (f_2 - q_k)\cos\theta)^2/b_k^2 \geq r \\ & p_k = [1.8, 2.8], \quad q_k = [1.8, 2.8], \quad a_k = [2, 2], \quad b_k = [8, 8], \quad k=1, 2 \\ & r = 0.1, \quad \theta = -0.25 \pi \\ & x_i \in [0, 1], \quad i = 1, \cdots, 30 \end{aligned} $$

Example

if __name__ == '__main__':
    main(300, 1000, np.array([0] * 30), np.array([1] * 30))
Output:

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