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PPS: Push and pull search

Reference: Fan Z, Li W, Cai X, et al. Push and pull search for solving constrained multi-objective optimization problems[J]. Swarm and Evolutionary Computation, 2019, 44: 665-679.
PPS is a constrained multi-objective evolutionary algorithm (CMOEA). In the push stage, a MOEA is used to explore the search space without considering any constraints. The pull stage handles constraints with a adjustable epsilon level.
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)
cp Control the speed of reducing relaxation of constraints (default = 2)
last_gen The last generation (default = 20)
CR Crossover rate (default = 1)
F Mutation scalar number (default = 0.5)
eta_m Spread factor distribution index (default = 20)
nvar The dimension of decision space
nobj The dimension of objective space
ideal The ideal points
nadir The nadir points
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
push_stage Denotes the search is in the push stage
rk The max rate of change between the ideal and nadir points during the last last_gen generations
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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