from typing import Callable, Optional
import torch
from evox.core import Algorithm, Mutable, Parameter
from evox.operators.crossover import simulated_binary
from evox.operators.mutation import polynomial_mutation
from evox.operators.sampling import uniform_sampling
from evox.operators.selection import ref_vec_guided
from evox.utils import clamp, nanmax, nanmin, randint
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class RVEA(Algorithm):
"""
A tensorized implementation of the Reference Vector Guided Evolutionary Algorithm (RVEA)
for multi-objective optimization problems.
:references:
[1] R. Cheng, Y. Jin, M. Olhofer, and B. Sendhoff, "A reference vector guided evolutionary algorithm
for many-objective optimization," IEEE Transactions on Evolutionary Computation, vol. 20, no. 5,
pp. 773-791, 2016. Available: https://ieeexplore.ieee.org/document/7386636
[2] Z. Liang, T. Jiang, K. Sun, and R. Cheng, "GPU-accelerated Evolutionary Multiobjective Optimization
Using Tensorized RVEA," in Proceedings of the Genetic and Evolutionary Computation Conference,
ser. GECCO ’24, 2024, pp. 566–575. Available: https://doi.org/10.1145/3638529.3654223
"""
def __init__(
self,
pop_size: int,
n_objs: int,
lb: torch.Tensor,
ub: torch.Tensor,
alpha: float = 2.0,
fr: float = 0.1,
max_gen: int = 100,
selection_op: Optional[Callable] = None,
mutation_op: Optional[Callable] = None,
crossover_op: Optional[Callable] = None,
device: torch.device | None = None,
):
"""Initialize the RVEA algorithm with the given parameters.
:param pop_size: The size of the population.
:param n_objs: The number of objective functions in the optimization problem.
:param lb: The lower bounds for the decision variables.
:param ub: The upper bounds for the decision variables.
:param alpha: A parameter for controlling the rate of change of penalty. Defaults to 2.
:param fr: The frequency of reference vector adaptation. Defaults to 0.1.
:param max_gen: The maximum number of generations. Defaults to 100.
:param selection_op: The selection operation for evolutionary strategy (optional).
:param mutation_op: The mutation operation (optional).
:param crossover_op: The crossover operation (optional).
:param device: The device on which computations should run (optional).
"""
super().__init__()
self.pop_size = pop_size
self.n_objs = n_objs
device = torch.get_default_device() if device is None else device
# check
assert lb.shape == ub.shape and lb.ndim == 1 and ub.ndim == 1
assert lb.dtype == ub.dtype and lb.device == ub.device
self.dim = lb.size(0)
# write to self
self.lb = lb.to(device=device)
self.ub = ub.to(device=device)
self.alpha = Parameter(alpha)
self.fr = Parameter(fr)
self.max_gen = Parameter(max_gen)
self.rv_adapt_every = Mutable(torch.max(torch.round(1 / self.fr), torch.tensor(1.0)))
self.selection = selection_op
self.mutation = mutation_op
self.crossover = crossover_op
self.device = device
if self.selection is None:
self.selection = ref_vec_guided
if self.mutation is None:
self.mutation = polynomial_mutation
if self.crossover is None:
self.crossover = simulated_binary
sampling, _ = uniform_sampling(self.pop_size, self.n_objs)
v = sampling.to(device=device)
v0 = v
self.pop_size = v.size(0)
length = ub - lb
population = torch.rand(self.pop_size, self.dim, device=device)
population = length * population + lb
self.pop = Mutable(population)
self.fit = Mutable(torch.full((self.pop_size, self.n_objs), torch.inf, device=device))
self.reference_vector = Mutable(v)
self.init_v = v0
self.gen = Mutable(torch.tensor(0, dtype=int, device=device))
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def init_step(self):
"""
Perform the initialization step of the workflow.
Calls the `init_step` of the algorithm if overwritten; otherwise, its `step` method will be invoked.
"""
self.rv_adapt_every = torch.max(torch.round(1 / self.fr), torch.tensor(1.0))
self.fit = self.evaluate(self.pop)
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def _rv_adaptation(self, pop_obj: torch.Tensor):
max_vals = nanmax(pop_obj, dim=0)[0]
min_vals = nanmin(pop_obj, dim=0)[0]
return self.init_v * (max_vals - min_vals)
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def _no_rv_adaptation(self, pop_obj: torch.Tensor):
return self.reference_vector.clone()
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def _mating_pool(self):
valid_mask = ~torch.isnan(self.pop).all(dim=1)
num_valid = torch.sum(valid_mask, dtype=torch.int32)
mating_pool = randint(0, num_valid, (self.pop_size,), device=self.pop.device)
sorted_indices = torch.where(valid_mask, torch.arange(self.pop_size, device=self.device), torch.iinfo(torch.int32).max)
sorted_indices = torch.argsort(sorted_indices, stable=True)
pop = self.pop[sorted_indices[mating_pool]]
return pop
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def _update_pop_and_rv(self, survivor: torch.Tensor, survivor_fit: torch.Tensor):
self.pop = survivor
self.fit = survivor_fit
self.reference_vector = torch.cond(
self.gen % self.rv_adapt_every == 0, self._rv_adaptation, self._no_rv_adaptation, (survivor_fit,)
)
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def step(self):
"""Perform a single optimization step."""
self.gen = self.gen + torch.tensor(1)
pop = self._mating_pool()
crossovered = self.crossover(pop)
offspring = self.mutation(crossovered, self.lb, self.ub)
offspring = clamp(offspring, self.lb, self.ub)
off_fit = self.evaluate(offspring)
merge_pop = torch.cat([self.pop, offspring], dim=0)
merge_fit = torch.cat([self.fit, off_fit], dim=0)
survivor, survivor_fit = self.selection(
merge_pop,
merge_fit,
self.reference_vector,
(self.gen / self.max_gen) ** self.alpha,
)
self._update_pop_and_rv(survivor, survivor_fit)
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